River, Sediment and Hydrological Extremes: Causes, Impacts and Management (Disaster Resilience and Green Growth) 981994810X, 9789819948109

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Disaster Resilience and Green Growth Series Editors: Anil Kumar Gupta · SVRK Prabhakar · Akhilesh Surjan

Manish Pandey Anil Kumar Gupta Giuseppe Oliveto   Editors

River, Sediment and Hydrological Extremes: Causes, Impacts and Management

Disaster Resilience and Green Growth Series Editors Anil Kumar Gupta, National Institute of Disaster Management, New Delhi, Delhi, India SVRK Prabhakar, Climate Change Adaptation, Institute of Global Environment Strategies, Kanagawa, Japan Akhilesh Surjan, College of Indigenous Futures, Arts and Society, Charles Darwin University, Darwin, Australia

Over the years, the relationship between environment and disasters has received significant attention. This is largely due to the emerging recognition that environmental changes-climate change, land-use and natural resource degradation make communities more vulnerable to disaster impacts. There is a need to break this nexus through environment based and sustainability inclusive interventions. Science – technology and economic measures for disaster risk management, hence, need to adapt more integrated approaches for infrastructure and social resilience. Environmental and anthropogenic factors are key contributors to hazard, risk, and vulnerability and, therefore, should be an important part of determining risk-management solutions. Green growth approaches have been developed by emphasizing sustainability inclusion and utilizing the benefits of science-technology interventions along policypractice linkages with circular economy and resource efficiency. Such approaches recognize the perils of traditional material-oriented economy growth models that tend to exploit natural resources, contribute to climate change, and exacerbate disaster vulnerabilities, Green growth integrated approaches are rapidly becoming as preferred investment avenue for mitigating climate change and disaster risks and for enhancing resilience. This includes ecosystem-based and nature-based solutions with potential to contribute to the resilience of infrastructure, urban, rural and periurban systems, livelihoods, water, and health. They can lead to food security and can further promote people-centric approaches. Some of the synergistic outcomes of green growth approaches include disaster risk reduction, climate change mitigation and adaptation, resilient livelihoods, cities, businesses and industry. The disaster risk reduction and resilience outcome of green growth approaches deserve special attention, both for the academic and policy communities. Scholars and professionals across the domains of DRR, CCA, and green growth are in need of publications that fulfill their knowledge needs concerning the disaster resilience outcomes of green growth approaches. Keeping the above background in view, the book series offers comprehensive coverage combining the domains of environment, natural resources, engineering, management and policy studies for addressing disaster risk and resilience in the green growth context in an integrated and holistic manner. The book series covers a range of themes that highlight the synergistic outcomes of green growth approaches. The book series aims to bring out the latest research, approaches, and perspectives for disaster risk reduction along with highlighting the outcomes of green growth approaches and including Science-technology-research-policy-practice interface, from both developed and developing parts of the world under one umbrella. The series aims to involve renowned experts and academicians as volume-editors and authors from all the regions of the world. It is curated and developed by authoritative institutions and experts to serve global readership on this theme.

Manish Pandey • Anil Kumar Gupta • Giuseppe Oliveto Editors

River, Sediment and Hydrological Extremes: Causes, Impacts and Management

Editors Manish Pandey Water Resources and Environmental Division, Department of Civil Engineering National Institute of Technology Warangal Warangal, India

Anil Kumar Gupta Centre for Excellence on Climate Resilience Environment Climate and DRM Division National Institute of Disaster Management New Delhi, India

Giuseppe Oliveto School of Engineering University of Basilicata Basilicata, Italy

ISSN 2662-4885 ISSN 2662-4893 (electronic) Disaster Resilience and Green Growth ISBN 978-981-99-4810-9 ISBN 978-981-99-4811-6 (eBook) https://doi.org/10.1007/978-981-99-4811-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

River, Sediment and Hydrological Extremes: Causes, Impacts and Management provides a comprehensive exploration of the intricate relationship between rivers, sediment dynamics, and hydrological extremes. This multidisciplinary work delves into the causes, impacts, and management strategies associated with these critical natural phenomena. This book brings together leading experts from various disciplines to shed light on the complex interplay between rivers, sediment transport, and hydrological extremes. It covers a wide range of topics, including the role of climate change, land-use practices, and natural hazards in shaping hydrological patterns. The book also examines the impacts of hydrological extremes on ecosystems, infrastructure, and human communities, emphasizing the need for adaptive and resilient management approaches. Moreover, River, Sediment and Hydrological Extremes delves into the intricate dynamics of sediment transport within river systems, exploring the linkages between sedimentation, erosion, and hydrological events. It highlights the significance of sediment as a valuable resource, while also addressing the challenges posed by excessive sedimentation, such as increased flood risk and reduced water quality. One of the remarkable aspects of this work is its focus on management strategies. The authors present a range of approaches aimed at mitigating the impacts of hydrological extremes, managing sediment transport, and fostering sustainable river basin management. These strategies encompass both engineering solutions and ecosystem-based approaches, recognizing the need for integrated and adaptive management frameworks.

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Overview of Book

The natural hazards posed by hydrologic events and river systems depend on the uncertainty of hydrological events. This ability is affected by change in climatic conditions. Climate change studies have revealed that the frequency of extreme weather phenomena with increasing damage to human assets has gradually grown worldwide. As a consequence, rainfall events concentrated in time and space are expected to lead to serious local flooding and sediment transport in many parts of the world. Floods are remarkable hydro-meteorological phenomena and forceful agents of geomorphic evolution in most physical geographical belts and, from the viewpoint of human society, among the most important environmental hazards. According to the Indian Environment Agency, floods rank as number one on the list of natural disasters in India over the past decade. Floods and excess rainfall also change the patterns of erosion and deposition that are ultimately determined by base level, the lowest elevation to which the river can flow. Base level, in turn, is set by the interplay between tectonic deformation of the land surface and sediment supply—quantities that can vary in space and time. The above concept has serious implications for understanding the recent development of the major river systems. Large rivers with high sediment load flow south from the Himalayas into a series of narrow valleys that run parallel to the mountain front. South Asia is one of the most risk-prone countries for river and sediment hazards. The geo-climatic variations of the region make the population vulnerable to flood and sediment and river-related disasters in varying degrees, intensities, and patterns. Sediment transport in rivers is one of the main causes of scouring and deposition and is always responsible for the failure of hydraulic structures and riverbank erosion. Therefore, precise estimation of erosion and scour is essential to reduce the hazards. Lack of preparedness and appropriate adaptation strategy makes people more riskprone. The South Asia region needs to be concerned about the impacts of flood, sediments, and river hazards because a large portion of its population depends on sensitive sectors like agriculture and forestry for livelihoods and several other reasons. Because of this, the book River, Sediment and Hydrological Extremes: Causes, Impacts and Management will cover such aspects. vii

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This book provides a platform for knowledge sharing in all areas related to rivers, sediment, and hydrological extremes. It will explain the hypothesis that river flow and sediment transport are intimately linked to erosion, scour, and sediment deposition. Sediment transport, erosion, and deposition are driven by local base-level changes and are highly variable in space and time. This book is divided into three parts: (1) introduction and overview, (2) causes and impacts, and (3) river restoration, hydraulic structure stability, and flood risk management. Under the Introduction and Overview part, Khundrakpam and Devi studied the Multi-Influencing Factor (MIF) (geospatial model), which is used for mapping and assessment of the flood-affected areas in the Iril River catchment of Manipur, India, for the period 2015–2021. The flood-affected area was observed to be highest in 2015, at 33.6 km2 (1.13%), followed by 32.5 km2 (1.09%) in 2017. Sudardeva and Pal studied the loss in Ecosystem Service Value (ESVs) under anthropogenic influences for Chennai and Hyderabad. They summarized the loss in ecosystem services that need urgent measures to enhance the sustainability of urban ecosystems through the restoration of waterbodies and effective land management practices. Bidyapati and Devi concluded that the vulnerability index of the Imphal East ranges from 130 to 173 (DRASTIC_AGRI) and 120 to 182 (DRASTIC_LU), indicating moderate to high vulnerability to groundwater contamination. Kiba et al. revealed that GIS can accurately predict the extent of flooding and produce flood maps, as well as flood damage estimation maps and flood hazard maps. Srikanth and Pal indicated that the spatiotemporal dynamics of meteorological variables could be used for long-term heatwave prediction, and both Support Vector Regression (SVR) and Random Forest (RF) models have the potential for reliable usage in this context. Kumari et al. suggested that Multi-Source Weather (MSWX) can be used in various climatic studies and hydrological modelling for areas or river basins where data are lacking or missing. Shukla focused on the behaviour of weakly nonlinear waves in mixed nonlinear fluids and further investigated the effect of van der Waals variables on wave evolution. Sharma and Swami presented a detailed study of the effects of measurement scale on temporal and spatial soil moisture analyses. The optimal measurement strategy for soil moisture measurement based on the optimal design for the study of spatiotemporal analysis will always be a trade-off between the accuracy and the cost of measurement. Patel and Sarkar concluded that the entropy-based approach could be utilized to determine the streamflow at any ungauged station on the Brahmani River, given the streamflow and stage at the upstream and downstream sites, respectively. Karna et al. observed that the deposition of fine sediments within the surface layer of bed material significantly impacts the aquatic life in the bed substrate. They utilized a numerical model to quantify the loss in the porosity of the surface layer bed material induced by this process. In the second part, Goodarzi et al. studied river water flow prediction, which has been made by two GEP and Random Forest (RF) machine learning algorithms. The results of the two models were compared using five statistical indices. Hussain and Pal evaluated spatiotemporal variations in drought and assessed its risk over

Overview of Book

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Telangana using satellite data. The comparison of the Standardized Soil Moisture Index (SSMI) with SPI and SPEI shows that SSMI, when compared with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), performs well in monitoring agricultural drought and can be used to develop effective drought warning and risk management. Okram and Devi studied drought-affected zones using satellite data and geographical information system (GIS) techniques in Thoubal district, Manipur (northeastern India), for the period 2013–2021. From the results, the study area was classified into five classes (severely dry, moderately dry, near normal, mildly wet, and moderately wet), and most of the study area experienced two drought conditions: moderate drought and near normal. Poonia et al. suggested that drought events across central and western parts of the country are severe and longer, whereas river basins in the southern part experience droughts more frequently but with low severity. The outcomes of this research offer crucial insights into drought hotspots with longer and more severe drought events across the study area and thus provide useful insights for policymakers to formulate comprehensive national drought mitigation and prevention strategies to safeguard a sustainable ecosystem. Barbhuiya et al. concluded that non-stationarity needs to be incorporated in the flood risk assessment framework for addressing the likely impacts of potential future climate change in water resources management. A comprehensive review of the different approaches for non-stationary flood frequency analysis is presented in this section. Gupta et al. quantified that the connection between extreme precipitation to moisture transport might help in the early prediction of extreme precipitation events over the Indian subcontinent. They evaluated the association between moisture transport and multiday extreme precipitation events by quantifying moisture transport during the identified top-ranked multi-day extreme precipitation events. Deeksha et al. investigated that the emphasis of modern urban studies is changing from interpretation to information collection for effective decision-making, which will help readers grasp the issues associated with the existing system and the way forward to achieve sustainable development. This study will assist stakeholders and policymakers in taking necessary actions to preserve the present ecological equilibrium. Gajulapalli et al. studied sustainable land and water management in urban areas, along with emerging challenges. The active incorporation of new decentralized technologies, green infrastructure, and low-impact development to ensure the long-term reliability and resilience of our water resources must be prioritized. Ojha et al. investigated three-dimensional octant analysis used to clarify the function of bursting events in the particle entrainment process. The outcomes of this study provide an important and detailed view of turbulent flow structures in vegetation and non-vegetation zones in open channel flow. Barman et al. studied about recirculation region control behind a partially submerged cylinder due to wave against current. Moreover, the mean flow, fluctuating velocity, and velocity derivatives interact and exchange energy in a complex manner in the lock-on case. Under river restoration, hydraulic structure stability, and flood risk management, Mishra and Tiwari obtained results that 4.106 mm3 or 11.93% of storage volume had been lost from the usable storage volume of the Kaliasote reservoir. The rate of

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sedimentation in the reservoir was also correlated with the empirical relationship of Varshney and Joglekar. Arjun et al. revealed better management of water resources in the Brahmaputra and Barak valleys in the Assam region needs detailed data and information about the river systems. However, the existing river database has a coarser resolution and lacks information, except for a few major rivers in Assam. Abhash et al. used Computational Fluid Dynamics (CFD) to simulate flow around a linear weir. The simulation results were compared with experimental results from the literature. The head-discharge relationship of the weir was also compared to the standard equations available in the literature. This study confirms the use of CFD as a tool for accurately predicting the flow patterns around hydraulic structures. Rathod et al. used an AI-based flood map generation tool for disseminating information and alerts to people in flood-prone areas. The system comprises several components, including hydrological and hydraulic models, AI-based flood map generation, and a mobile application for real-time alerts and geolocation-based messaging. This approach is scalable, cost-effective, and allows real-time monitoring for immediate responses to changing conditions, reducing the impact of floods and mitigating the risk of property damage and loss of life. Amin et al. studied extreme events that are part of the natural environment, creating diverse habitats through erosion and deposition processes. Human-induced climate change is predicted to increase average temperatures, leading to an increase in variables; therefore, a well-developed sustainable approach to managing risks is needed for the integrity of nature.

Contents

Part I 1

2

3

4

5

6

Introduction and Overview

Flood Modeling Using MIF Method with GIS Techniques: A Case Study of Iril River Catchment, Manipur, India . . . . . . . . . . Sandhip Khundrakpam and Thiyam Tamphasana Devi A Case Study on Estimating the Ecosystem Service Values (ESVs) Under Anthropogenic Influences for Chennai and Hyderabad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sudardeva and Manali Pal Groundwater Vulnerability Mapping Using Modified DRASTIC Model: A GIS-Based Case Study of Imphal East District, Manipur, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haobam Bidyapati and Thiyam Tamphasana Devi

3

23

41

Flood Hazard Mapping Using Hydraulic Models and GIS: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liza G. Kiba, Grace Nengzouzam, and Prem Ranjan

65

A Case Study on the Prediction of Heatwave Days Using Machine Learning Algorithms over Telangana . . . . . . . . . . . . . . . . B. Srikanth and Manali Pal

73

Quantifying the Reliability of Reanalysis Precipitation Products Across India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alka Kumari, Akash Singh Raghuvanshi, and Ankit Agarwal

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7

Dynamics of Weakly Nonlinear Waves Propagating in the Region with Mixed Nonlinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Triveni P. Shukla

8

Spatial and Temporal Variability of Soil Moisture, Its Measurement and Methods for Analysis: A Review . . . . . . . . . . 131 Sahil Sharma and Deepak Swami xi

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Part II

Causes and Impacts

9

Streamflow Estimation Using Entropy-Based Flow Routing Technique in Brahmani River, Odisha . . . . . . . . . . . . . . . . . . . . . . 167 Pooja Patel and Arindam Sarkar

10

Infiltration of Suspended Fine Sediments into Surface Layer of Coarse Sediment-Bedded Channel . . . . . . . . . . . . . . . . . . . . . . . . 183 Nilav Karna, A. S. Lodhi, Sai Guguloth, and Ankit Chakravarti

11

River Water Flow Prediction Rate Based on Machine Learning Algorithms: A Case Study of Dez River, Iran . . . . . . . . . . . . . . . . . 203 Mohammad Reza Goodarzi, Amir Reza R. Niknam, Ali Barzkar, and Davood Shishebori

12

A Case Study in Evaluating Spatiotemporal Variations in Drought and Its Risk Assessment over Telangana Using Satellite Data . . . . . 221 Palagiri Hussain and Manali Pal

13

Drought Modeling Through Drought Indices in GIS Environment: A Case Study of Thoubal District, Manipur, India . . . . . . . . . . . . . 235 Denish Okram and Thiyam Tamphasana Devi

14

Copula-Based Probabilistic Evaluation of Meteorological Drought Characteristics over India . . . . . . . . . . . . . . . . . . . . . . . . . 257 Vikas Poonia, Lixin Wang, and Manish Kumar Goyal

15

Nonstationary Flood Frequency Analysis: Review of Methods and Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Siddik Barbhuiya, Meenu Ramadas, and Shanti Swarup Biswal

16

Multiday Extreme Precipitation Ranking and Association with Atmospheric Moisture Transport During Indian Summer Monsoon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Hariom Gupta, Akash Singh Raghuvanshi, and Ankit Agarwal

Part III

River Restoration, Hydraulic Structure Stability and Flood Risk Management

17

Remote Sensing and Its Application on Soil: An Ecosystem Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Deeksha, Anoop Kumar Shukla, Nandineni Rama Devi, and Satyavati Shukla

18

Sustainable Land and Water Management in Urban Areas: Emerging Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Suryanarayana Gajulapalli, Sumanth Chinthala, and Sridhar Pilli

Contents

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19

Nature of Bursting Events over a Rigid Bed with Emergent Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Aaditya Ojha, Abhishek Kumar, Pritam Kumar, and Anurag Sharma

20

Recirculation Region Control Behind a Partially Submerged Cylinder Due to Wave Against Current . . . . . . . . . . . . . . . . . . . . . 349 Krishnendu Barman, Sayahnya Roy, Susanta Chaudhuri, and Koustuv Debnath

21

Assessment of Sedimentation in Kaliasote Reservoir, Bhopal, Using Satellite Remote Sensing Techniques . . . . . . . . . . . . . . . . . . . 365 K. Mishra and H. L. Tiwari

22

Development of River Atlas Using Space and Ground-Based Inputs for Brahmaputra and Barak Valleys in Assam, India . . . . . . 377 B. M. Arjun, Diganta Barman, Gokul Anand, Nilay Nishant, Anupal Baruah, Biren Baishya, and S. P. Aggarwal

23

Numerical Study of Flow Through Linear Weir . . . . . . . . . . . . . . . 397 Amiya Abhash, Ravi Prakash Tripathi, Padam Jee Omar, Nitesh Gupta, and K. K. Pandey

24

Artificial Intelligence-Based Fully Scalable Real-Time Early Flood Warning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Praveen Rathod, Manish Pandey, and Anil Kumar Gupta

25

Sustainability Through Integrated Resilience and Risk Management: Rivers and Disasters in Changing Climate . . . . . . . . 417 Fatima Amin, Mushtaq Ahmad Dar, and Anil Kumar Gupta

Editors and Contributors

About the Editors Manish Pandey graduated in Civil Engineering from Uttarakhand Technical University, India. He completed his master’s and doctorate from Indian Institute of Technology Roorkee, India. Presently, Dr. Pandey is Assistant Professor at NIT Warangal since 2019. He has more than 5 years of teaching and research experience in experimental hydraulics and water resources engineering. He has authored more than 40 peer-reviewed journal papers and 20+ book chapters and conference proceeding papers. He has guided one PhD and five M.Tech students. Presently he is guiding four PhD and five M.Tech students. He was also awarded MOST postdoctoral research grant in 2018. He is an editorial board member of various national and international journals. Dr. Pandey is an active reviewer of several reputed peerreviewed journals. Anil Kumar Gupta is a sustainability risk management strategist working in the area of disaster management, environment, and climate resilience for more than 25 years with national, sub-national, and business administrations. He is currently a full Professor and Head of India’s National Institute of Disaster Management (NIDM) Division of Environment and Disaster Risk Management. He is Programme Director of the Centre for Excellence on Climate Resilience and implementing projects, viz. CAP-RES (with DST, under National Knowledge Mission on Climate Change) and National Agriculture Disaster Management Plan (with Ministry of Agriculture & Farmer’s Welfare). He was a recipient of Excellence Award by the Society of Environmental & Occupational Health, and bestowed with IDRC Canada’s Think Tank Initiative Senior Fellowship 2011 for policy research. Giuseppe Oliveto is Associate Professor of Hydraulic Engineering at the School of Engineering of University of Basilicata (Italy). Since 1992 he has been carrying out theoretical and experimental studies encompassing evolution and patterns of river networks, fluvial hydraulics, sediment transport, bridge hydraulics, and urban drainage hydraulics. He is the author of more than 100 papers in scientific journals and xv

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conference proceedings. He is an editorial board member of various national and international journals. He was recognized as an outstanding reviewer by international journals. He has been awarded the Robert Alfred Carr Prize by the Council of the Institution of Civil Engineers (ICE), London (UK), for the paper “Temporal scour evolution at non-uniform bridge piers” published in the Proceedings of the Institution of Civil Engineers - Water Management, Volume 170, October 2017.

Contributors Amiya Abhash Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India Ankit Agarwal Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India S. P. Aggarwal North Eastern Space Applications Centre, Umiam, Meghalaya, India Fatima Amin Environment, Climate and Disaster Risk Management (ECDRM) National Institute of Disaster Management, (NIDM) Ministry of Home Affairs, Govt. of India, New Delhi, India Gokul Anand North Eastern Space Applications Centre, Umiam, Meghalaya, India B. M. Arjun North Eastern Space Applications Centre, Umiam, Meghalaya, India Biren Baishya Assam State Disaster Management Authority, Guwahati, Assam, India Siddik Barbhuiya School of Infrastructure, Indian Institute of Technology, Bhubaneswar, Odisha, India Diganta Barman North Eastern Space Applications Centre, Umiam, Meghalaya, India Krishnendu Barman Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore, India Anupal Baruah North Eastern Space Applications Centre, Umiam, Meghalaya, India Ali Barzkar Department of Civil Engineering, Water Resources Management Engineering, Yazd University, Yazd, Iran Haobam Bidyapati Department of Civil Engineering, National Institute of Technology, Imphal, Manipur, India Shanti Swarup Biswal School of Infrastructure, Indian Institute of Technology, Bhubaneswar, Odisha, India

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Ankit Chakravarti School of Civil and Environmental Engineering, Hachalu Hundessa Technology Campus, Ambo University, Ambo, Ethiopia Susanta Chaudhuri Department of Geological Sciences, Jadavpur University, Kolkata, India Sumanth Chinthala Department of Civil Engineering, NIT, Warangal, Telangana, India Mushtaq Ahmad Dar Department of School Education J&K UT, Srinagar, Jammu and Kashmir, India Koustuv Debnath Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India Deeksha Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal, Karnataka, India Thiyam Tamphasana Devi Department of Civil Engineering, National Institute of Technology, Imphal, Manipur, India Suryanarayana Gajulapalli Department of Civil Engineering, NIT, Warangal, Telangana, India Mohammad Reza Goodarzi Department of Civil Engineering, Yazd University, Yazd, Iran Manish Kumar Goyal Department of Civil Engineering, Indian Institute of Technology Indore, Indore, India Sai Guguloth Department of Civil Engineering, National Institute of Technology Warangal, Warangal, India Anil Kumar Gupta Centre for Excellence on Climate Resilience, Environment Climate and DRM Division, National Institute of Disaster Management, New Delhi, India Hariom Gupta Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Nitesh Gupta Department of Civil Engineering, Nirma University, Ahmedabad, India P. Hussain Department of Civil Engineering, NIT, Warangal, Telangana, India Nilav Karna AECOM, New Delhi, India Sandhip Khundrakpam Department of Civil Engineering, National Institute of Technology, Manipur, India Liza G. Kiba Department of Agricultural Engineering, SASRD, Nagaland University, Medziphema, India

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Abhishek Kumar Department of Civil Engineering, National Institute of Technology, Rourkela, India Pritam Kumar Department of Civil Engineering, National Institute of Technology, Rourkela, India Alka Kumari Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India A. S. Lodhi Soil and Water Engineering Department, Jawaharlal Nehru Krishi Vishwa Vidhyalaya, Jabalpur, Madhya Pradesh, India K. Mishra Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India Grace Nengzouzam Department of Agricultural Engineering, SET, Nagaland University, Dimapur, India Amir Reza R. Niknam Department of Civil Engineering, Water Resources Management Engineering, Yazd University, Yazd, Iran Nilay Nishant North Eastern Space Applications Centre, Umiam, Meghalaya, India Aaditya Ojha Department of Civil Engineering, National Institute of Technology, Rourkela, India Denish Okram Department of Civil Engineering, National Institute of Technology, Manipur, India Padam Jee Omar FLoodkon Consultants LLP, Noida, India Manali Pal Department of Civil Engineering, NIT, Warangal, Telangana, India K. K. Pandey Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India Manish Pandey NIT, Warangal, India Pooja Patel School of Infrastructure, IIT Bhubaneswar, Argul, Khordha, Odisha, India Sridhar Pilli Department of Civil Engineering, NIT, Warangal, Telangana, India Vikas Poonia Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA Akash Singh Raghuvanshi Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Meenu Ramadas School of Infrastructure, Indian Institute of Technology, Bhubaneswar, Odisha, India

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Nandineni Ramadevi Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal, Karnataka, India Prem Ranjan Department of Agricultural Engineering, NERIST, Nirjuli, Arunachal Pradesh, India Praveen Rathod Neram Civil Environmental Solution Pvt. Ltd., Mumbai, India Sayahnya Roy Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India Arindam Sarkar School of Infrastructure, IIT Bhubaneswar, Argul, Khordha, Odisha, India Anurag Sharma Department of Civil Engineering, National Institute of Technology, Rourkela, India Sahil Sharma School of Engineering, Indian Institute of Technology, Mandi, India Davood Shishebori Department of Industrial Engineering, Yazd University, Yazd, Iran Anoop Kumar Shukla Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal, Karnataka, India Satyavati Shukla Key Laboratory of Geospatial Informatics, Guilin University of Technology, Guilin, People’s Republic of China Triveni P. Shukla Department of Mathematics, National Institute of Technology, Warangal, Telangana, India B. Srikanth Department of Civil Engineering, NIT, Warangal, Telangana, India Sudardeva Department of Civil Engineering, NIT, Warangal, Telangana, India Deepak Swami School of Engineering, Indian Institute of Technology, Mandi, India H. L. Tiwari Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India Ravi Prakash Tripathi Department of Civil Engineering, Rajkiya Engineering College, Sonbhadra, Uttar Pradesh, India Lixin Wang Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA

Part I

Introduction and Overview

Chapter 1

Flood Modeling Using MIF Method with GIS Techniques: A Case Study of Iril River Catchment, Manipur, India Sandhip Khundrakpam and Thiyam Tamphasana Devi

Abstract In the present study, a multi-influencing factor (MIF) (geospatial model) is used for mapping and assessment of the flood-affected areas in the Iril River catchment of Manipur, India, for the period of 2015–2021. The study region is in the plain valley part of the state, which is frequently prone to flooding due to its topographical landscape and rapid urbanization in recent years. In the MIF method, a major and minor influence is used to inter-relate the parameters and weight is calculated by using MIF score formula. Six parameters were used in MIF method, that is , slope, soil type, drainage density, rainfall, topographical wetness index (TWI), and NDVI (normalized vegetation index). Then each parameter is reclassified into five subclasses and ranking of 1–5 (low to high) is assigned to each subclass of the parameters. The predicted flood-affected areas were divided into four categories: very low, low, moderate, and high. The study region was found to be mostly affected by low to moderate flood (approximately 97%) in every year of the study period (2015–2021), which may not be a cause for concern. However, in terms of the magnitude of flood caused by the high category (as compared to the other flood classes), it was observed that the flood-affected area was highest in 2015, at 33.6 km2 (1.13%), followed by 32.5 km2 (1.09%) in 2017. And lower flood risk is thus observed in 2019 (0.74%) and 2021 (0.79%), respectively. Particularly, the predicted results for the year 2015 were compared and validated with literature and collected data, and a similar flood pattern was observed in this year. Keywords GIS technique · MIF method · Weighted overlay · Iril catchment · GIS techniques

S. Khundrakpam · T. T. Devi (✉) Department of Civil Engineering, National Institute of Technology, Manipur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Pandey et al. (eds.), River, Sediment and Hydrological Extremes: Causes, Impacts and Management, Disaster Resilience and Green Growth, https://doi.org/10.1007/978-981-99-4811-6_1

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1.1

S. Khundrakpam and T. T. Devi

Introduction

Rivers, sediment, and hydrological extremes are interlinked components of the Earth’s natural systems that have significant impacts on the environment and human societies (Das and Umamahesh 2022). The causes of river sedimentation and hydrological extremes, such as floods (Saikumar et al. 2022) and droughts, are complex and can be influenced by various natural and human-induced factors, including climate change (Sinha et al. 2020; Das and Umamahesh 2018), land use changes, and water management practices. These processes can have significant impacts on river ecosystems, water quality, infrastructure, and human livelihoods. Understanding the drivers and impacts of river sedimentation and hydrological extremes is crucial for effective management strategies. Integrated approaches that consider the interactions between rivers, sediment, and hydrological extremes are essential for sustainable river basin management. This may involve measures such as river channel modifications, sediment trapping structures, floodplain zoning, and effective water resource management to mitigate the adverse impacts of these processes on the environment and society. Additionally, incorporating local knowledge, stakeholder engagement, and participatory approaches are critical for developing robust management plans that account for the diverse needs and perspectives of communities living in riverine areas. Flooding is a primary natural disaster (Khan et al. 2011) and an event of hydrological extremes that has been occurring in all parts of the world. Excess water overflows the rivers and lakes to cause flooding, and several forms of sediment are transported along with the excess flow. Sediments deposited on the downstream side of the river is the major cause of disturbance in the ecosystem of water resource management (Jonkman and Dawson 2012; Kondolf et al. 2014). To solve most of the problems faced in management of hydrological extreme events such as flood or drought is the rightfully management of siltation and sediment deposition of river environment (Hauer et al. 2018). When a flood occurs, it not only takes the lives of humans and animals, but it also has long-term impacts on the ecosystem of living things. The infrastructure damage caused by the flood cannot be revived to its normal life expectancy, which again interrupts the various policies and programs planned by the government or other stakeholders and, therefore, significantly affects the overall economy of the region or state (Lechowska 2018). Timely management and frequent monitoring with preventive measures for flooding are becoming crucial in areas where flood events are common (Behera and Devi 2022). With advanced technology, tools, and techniques along with the satellite imagery, flood events can be predicted or modeled (Munawar et al. 2022). And one of the techniques for flood modeling is using GIS tools with satellite data, and within this GIS platform (Sinha et al. 2008; Khan et al. 2011; Ajin et al. 2013; Ouma and Tateishi 2014; Hamdi et al. 2019; Hammami et al. 2019; Dash and Sar 2020), there are several multi-criteria decision-making methods (MCDM) such as AHP (analytic hierarchy process) (Danumah et al. 2016; Souissi et al. 2020; Senan et al. 2023) and MIF (multi-influencing factors) (Taheri et al. 2020; Singh et al.

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Flood Modeling Using MIF Method with GIS Techniques: A Case Study of. . .

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2021; Singh and Devi 2022) which are commonly used for modeling of hydrological extreme events (flood and drought). The impacts of the multi-influencing factor (MIF) method on river, sediment, and hydrological extremes (Das and Umamahesh 2017; Bronstert et al. 2018; Jarajapu et al. 2022) are manifold. It helps in identifying the vulnerabilities of river and sediment systems to different factors and their interactions. It can also provide insights into the cascading effects of changes in one factor on other factors, allowing for more informed decision-making in river and sediment management. Additionally, the MIF method aids in identifying potential mitigation measures and interventions to reduce the negative impacts of human activities on rivers and sediment systems. MIF method enables a better understanding of the underlying causes of floods, such as changes in precipitation patterns due to climate change, alterations in land use and land cover, and modifications to river channels and floodplains. This understanding can help in identifying areas that are more vulnerable to floods and areas where flood risk may be increasing due to human activities. This information can inform flood mapping efforts (Mangukiya and Sharma 2022) and help in prioritizing resources for flood management. Mitra et al. (2022) conducted an assessment on the performance of the GIS-based AHP method for flood modeling in the Dinajpur District of West Bengal. In their study, it was observed that 20% is categorized as high flood-risk and 27% as medium flood-risk zone in the district. They concluded that the limitations of MCDM can be improved using high-resolution satellite data, selecting suitable methods for a specific region, conducting sensitivity analysis, and applying machine learning techniques. Despite having such limitations, MCDM can be used successfully and reliably for flood modeling in any region. MIF method is mostly used for drought modeling (Pandey et al. 2021), groundwater modeling (Magesh et al. 2012; Anbarasu et al. 2020; Borah and Deka 2022), and very few in other areas such as suitability assessment for urban settlement (Singh et al. 2021). As the concept of MIF method is similar with other MCDM, there will be no exponential error in applying this method for flood modeling. Therefore, in the present study, an attempt is made to check the performance of MIF method in flood modeling. Thus, in this study, flood risk mapping and assessment (for the years 2015, 2017, 2019, and 2021) using the MIF method in a GIS environment is conducted in the Iril River catchment of Manipur (the north-eastern part of India).

1.2

Study Area

The study area (Iril catchment) is located in the districts of Senapati, Imphal east and west, and Ukhrul of Manipur state (northeastern part of India) shown in Fig. 1.1. Geographical area lies between latitude 24°40′ N to 25°25′ N and longitude 93° 55′ E to 94°20′ E. The catchment area is estimated to be around 2985.5 km2. The pour point to delineate the catchment area is taken at the Lamboikhul Tiger Camp (on the riverbed under the Eereima Suspension Bridge) having latitude 24°55′58.38″

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S. Khundrakpam and T. T. Devi 94°0'0"E

India

94°10'0"E

94°20'0"E

Iril river catchment

93°35'0"E

94°45'0"E

25°10'0"N

25°0'0"N

24°20'0–N

Manipur

25°0'0"N

25°30'0"N

94°45'0"E

24°20'0–N

25°30'0–N

93°35'0"E

25°10'0"N

25°20'0"N

25°20'0"N

N

Iril pour point 94°0'0"E

94°10'0"E

94°20'0"E

Fig. 1.1 Study area (Iril River catchment)

N and longitude 94°2′46.41″E which is marked in Fig. 1.1. As the study region is valley area of the state, the ecosystem of water resource management is affected by major rivers and its tributaries. In the state, there are 15 major rivers and streams (166.77 km2 which is around 0.75% of the total geographical area). The Barak river basin (Barak valley) to the west, the Manipur river basin in Central Manipur, the Yu river basin in the east, and a portion of the Liyai river basin in the north are the major river basins of the state, and urban drinking water supply is mostly (90%) from these rivers.

1.3

Data Used and Method

The collected data and its sources are provided in Table 1.1. The collected data are DEM (digital elevation model), Landsat 8/9 (2017, 2019, 2021) from USGS (US Geological Survey), Earth Explorer, rainfall (Directorate of Environment and Climate Change, Government of Manipur), and ESDAC (European Soil Data Centre). The Landsat 8/9 data were taken for the month of October of the studied period. The data collected from different sources (Table 1.1) is utilized and processed through image processing for the required format using GIS tools (ArcGIS10.3®). In order to generate the secondary data from the primary data, IDW (inverse distance weighting) algorithm is used which is within the GIS platform. Then, the generated parameters, that is, rainfall, slope, soil type, drainage density, TWI (topographic wetness index), and NDVI (normalized difference vegetation index) are converted to

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Table 1.1 Data used with its source Sl. No. 1.

Data DEM

Source USGS, Earth Explorer

Resolution Spatial (30 m)

2.

NDVI

3.

Rainfall

4. 5

TWI Soil type

USGS, Earth Explorer Landsat 8/9 Directorate of Environment and Climate Change USGS, Earth Explorer ESDAC (European Soil Data Centre)

Temporal (2015, 2017, 2019, 2021) Temporal (2015, 2017, 2019, 2021) Spatial (30 m) Spatial (30 m)

Extracted data Slope, drainage density NDVI Rainfall TWI Soil type of study Iril catchment

Fig. 1.2 Flowchart of methodology

raster format and the MIF score is calculated and overlaid by giving their respective weights to generate the flood risk map (for the years 2015, 2017, 2019, and 2021). The flowchart of methodology is provided in Fig. 1.2.

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1.4

S. Khundrakpam and T. T. Devi

MIF Method

Prediction and mapping of flood-affected zones in the study region using MIF method includes collection of required data from different sources, generation of database (selection of input parameters), and finally developing flood risk map. MIF method is used by computing the interrelationship between the influencing factors which are categorized by two factors, that is, major (B) and minor (A) influence, and MIF score or weight is calculated by using Eq. 1.1. Influential factors are considered to assign the weight to each parameter. Figure 1.3 shows the interrelationship between these effects and their factor. Major effect represents direct influence of one factor over another, and minor effect represents indirect influence. The major and minor effects are classified based on their holding capacity and the characteristics of the surface and subsurface features. The major factor is assigned a value of 1 and minor factor is assigned as 0.5. These values are combined to calculate the MIF score of each layer (parameters) using the following equation: MIF score =

1.5

ðA þ B Þ × 100 ð A þ BÞ

ð1:1Þ

MIF Weights

The major purpose of MIF method is to give the weightage to the given parameters which is affecting to cause flood. The effectiveness of the quality of prioritization has a direct impact on the available resources. In most situations, the decision-maker’s primary judgment is used. In this study, experts’ and decision-makers’ “technical skills and know how” to solve the problems are considered. Several field surveys Fig. 1.3 Interrelationship of parameters by MIF method

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Flood Modeling Using MIF Method with GIS Techniques: A Case Study of. . .

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Table 1.2 Effect of different influencing factors and their corresponding scores Factor NDVI Slope TWI Drainage density Soil type Rainfall

Minor effect (A) 0.5 0 0.5 0.5 0.5 + 0.5 0.5

Major effect (B) 0 1 1 1+1 1+1 1+1+1

(A + B) 0.5 1 1.5 2.5 3 3.5 ∑ = 12

Weight 4 8 12 21 25 30 ∑ = 100

were conducted in and around the study area interacting with the people of local communities. With the understanding resulted from the community interaction and consultation with them, the weights are assigned to the selected input parameters on the scale of 1–5 (very low to very high class). Table 1.2 shows the effect caused by different influencing factors (minor effect, A, and major effect, B), and the weight is calculated by using Eq. 1.1. The calculated weights are provided in Table 1.3 and each parameter is reclassified into five classes. Using the assigned ranks and weights, these thematic layers (input parameters) are overlaid by using weighted overlay method, and finally a flood risk map (flood-affected area) is generated for the years 2015, 2017, 2019, and 2021.

1.6

Input Parameters: Theoretical Background

Six input parameters are considered for the flood modeling in this study, and the theoretical concept of each parameter is provided in the following section.

1.6.1

Slope

The slope of a terrain is a critical aspect in determination of its dependability and is a measure of its steepness of any plane. The direction and quantity of surface runoff or subsurface drainage which reaches an area are determined by the slope. The contribution of rainfall to stream flow is dominated by slope. It regulates the length of overland flow, infiltration, and subterranean flows which are all examples of flow. Slope is presented in percentage and calculated as “rise” divided by “run” multiplied by 100. Slope is also generally expressed in degrees.

Units Level %

Level

km/km2

Level mm

Criteria NDVI Slope

TWI

Drainage density

Soil type Rainfall

Year 2015 -1–0 0–6 6–16 16.01–40 40.01–60 60.1–183.8 -0.58–5 5.1–9 9.1–11 12–19 20–21 0–2 2.1–4 4.1–4.5 Clay Loam 59.87–402.7 402.8–642.7 642.8–833.7 833.8–1030 1031–1309 2017 -1–0 0–6 6–16 16.01–40 40.01–60 60.1–183.8 -0.58–5 5.1–9 9.1–11 12–19 20–21 0–2 2.1–4 4.1–4.5 Clay Loam 83.3–600 601–1000 1000–1400 1410–1700 1710–2230

Table 1.3 Assigned MIF rankings and weights for flood mapping 2019 -1–0 0–6 6–16 16.01–40 40.01–60 60.1–183.8 -0.58–5 5.1–9 9.1–11 12–19 20–21 0–2 2.1–4 4.1–4.5 Clay Loam 761–914 915–1010 1020–1090 1100–1160 1170–1240

2021 -1–0 0–6 6–16 16.01–40 40.01–60 60.1–183.8 -0.58–5 5.1–9 9.1–11 12–19 20–21 0–2 2.1–4 4.1–4.5 Clay Loam 1040–1100 1100–1140 1150–1180 1190–1210 1220–1250 Ranges Very high Very high High Moderate Low Very low Very high High Moderate Low Very low Very low Low Moderate Moderate Very low Low Moderate High Very high

Class rating 5 5 4 3 2 1 1 2 3 4 5 1 2 3 3 1 2 3 4 5

25 30

21

12

Weight (%) 4 8

10 S. Khundrakpam and T. T. Devi

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Flood Modeling Using MIF Method with GIS Techniques: A Case Study of. . .

1.6.2

11

Drainage Density

The physical characteristics of a catchment area are described through drainage density. Thus, the potential of water carried over by the landscape is related to drainage density and is an important parameter for understanding the ecosystem of water resource management in that region. If the density is high, the catchment region will be more prone to degradation, ending in deposition on the deeper grounds. It is calculated as stream length divided by basin area and its unit is km/km2.

1.6.3

Soil Type

Soil texture is an essential component and property of soils. Based on soil texture, soil types are classified primarily as sand, silt, and clay. Clay soils are far less transparent and hold water for a greater amount of time than sandy soils. It demonstrates that the locations with clay soils are more prone to flood. When measurements are unavailable, the feel and appearance of the soil can be used to infer soil moisture. Soil moisture serves as the boundary between the land surface and atmosphere, and it is important in the division of rainfall into runoff and water storage in groundwater.

1.6.4

TWI

Topographic wetness index (TWI) is commonly used to quantify topographic control on hydrological processes. TWI (Nsangou et al. 2022) indicates the amount of water that is accumulated on a specific area and expressed as index. Its high value gives high potential and low value gives low potential of water accumulation. It ranges from -3 to 30 and it is calculated as TWI = ln

U as tan β

ð1:2Þ

where Uas is the area contributing to its upstream side and β is slope gradient.

1.6.5

Rainfall Distribution

Heavy rains, which prohibit natural watercourses from channeling surplus water, are the most prevalent primary cause of flood. The amount of runoff generated by a catchment is related to the amount of rain received in that catchment. Heavy rains

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S. Khundrakpam and T. T. Devi

raise the level of water in rivers and lakes abruptly. When the water level exceeds the riverbanks or dams, the water begins to overflow, resulting in river-based flood. Volume of flood is contributed by the overflows from all the water bodies during heavy and continuous rainfall.

1.6.6

NDVI

The NDVI index analyzes and assesses the presence of live greenery using reflected light in the visible (VIS) and near-infrared wavelengths (NIR). Simply, NDVI is a gauge of the greenness of vegetation, as well as its richness and health. Thus, NDVI is calculated as NDVI =

1.7

ðNIR - VISÞ ðNIR þ VISÞ

ð1:3Þ

Result and Discussion

In this section, the generated input parameters with the finally modeled flood risk map will be presented and accordingly will discuss the result.

1.7.1

Result

1.7.1.1

Generated Input Parameters

Generated slope (%, which is “rise” divided by “run” multiplied by 100) and drainage density (km/km2) map is provided in Fig. 1.4. Drainage density is high for the major streams (Iril river) and nearby to the river and its tributaries. Soil type (Fig.1.5a) was generated using ESDAC (European Soil Data Centre) data and classified as clay loamy soil, and TWI is shown in Fig.1.5b. It is observed that the entire study region is covered by clay loam soil, which has the combined properties of low drainage capacity, moderate fertility, and good waterholding potential. By IDW method, the rainfall ranges of the given study area are derived and provided in Fig. 1.6 for the years 2015, 2017, 2019, and 2021, respectively. High rainfall is concentrated mostly in northern part of the study region in all the studied years. Highest rainfall goes up to around 2230 mm in a year which is observed in the year 2017 followed by 1309 mm in the year 2015. Medium rainfall is observed in 2019 and 2021; and lowest is observed in the year 2015 (60 mm per year) followed

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Flood Modeling Using MIF Method with GIS Techniques: A Case Study of. . .

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Fig. 1.4 (a) Slope and (b) drainage density

Fig. 1.5 (a) Soil type and (b) TWI

by in the year 2017 which is around 83 mm. So there is a large uneven distribution of rainfall in these years which suggest uncertain climatic conditions. However, in the years 2019 and 2021, the minimum rainfall observed is around 761 mm–1040 mm

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S. Khundrakpam and T. T. Devi

Fig. 1.6 Rainfall for the years (a) 2015, (b) 2017, (c) 2019, and (d) 2021

and maximum is 1240 mm–1250 mm, respectively, which indicates moderate climate conditions as per web report of the Indian Meteorological Department (IMD 2022). Then, the NDVI (range of -1–0) of these years have been calculated and found that it has less vegetation at the month of October (Fig. 1.7) which is the month of every year taken (temporal scale) in this study.

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Flood Modeling Using MIF Method with GIS Techniques: A Case Study of. . .

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Fig. 1.7 NDVI for the years (a) 2015, (b) 2017, (c) 2019, and (d) 2021

1.7.1.2

Prediction of Flood

The predicted flood risk map for the years 2015, 2017, 2019, and 2021 is shown in Fig.1.8a–d. Flood-affected zone is classified as very low, low, moderate, and high.

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S. Khundrakpam and T. T. Devi

Fig. 1.8 Flood risk map for the years (a) 2015, (b) 2017, (c) 2019, and (d) 2021

Table 1.4 shows the predicted flood area in km2 and percentage (%) for the years 2015, 2017, 2019, and 2021. Northwest side of the study region are more prone to flood as compared to southeast side. From Fig.1.8, in all the years (2015, 2017, 2019, and 2021), it is affected by low to moderate flood and very small areas by high flood. Figure 1.9 shows the area of

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Flood Modeling Using MIF Method with GIS Techniques: A Case Study of. . .

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Table 1.4 Predicted flood-affected area

Flood class Very low Low Moderate High

Flood-affected area (km2) 2015 2017 39.2 49.00 1941.9 1900.3 970 1003.7 33.6 32.5

2019 72.00 1973.2 918.3 22

2021 58.30 1957.9 945.7 23.6

(%) 2015 1.32 65.04 32.49 1.15

2017 1.64 63.66 33.62 1.08

2019 2.41 66.09 30.76 0.74

2021 1.95 65.58 31.68 0.79

Fig. 1.9 Predicted flood affected area (%)

predicted flood in percentage (%). But the high category flood is the most significant to cause damage to agricultural activities as well as to properties. Thus, it is effective to study further, selecting only the flood-affected areas classified as high risk in the present study. Therefore, the flood-affected area in 2015 is observed to be the highest, at 33.6 km2 (1.15%), followed by the year 2017 at 32.5 km2 (1.28%) in the category of high flood as compared with other years, which is shown in Fig. 1.9.

1.8

Discussion

By considering multiple influencing factors such as NDVI, TWI, rainfall, slope, drainage density, and soil type, we can create a more comprehensive flood mapping model that takes into account various hydrological, topographic, and environmental factors that affect flood dynamics. Integrating these factors into a combined approach can provide a more accurate and holistic understanding of flood-prone areas, which can be valuable for flood risk assessment, preparedness, and management. It was found out that flood is affected mostly by rainfall with MIF score of 30 which gives more weightage to it and NDVI is less affected with a score of 4.

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Moreover, the study region is in the valley plain part of the state where its terrain is 90% hilly. Soil erosion or soil loss from the sloppy hills is a frequent phenomenon during rainy seasons, and these sediment particles flow down to the plain valley through streams, rivers, and its tributaries as river discharge. High-intensity rainfall causes heavy landslide as most of the soil types in the state is alluvial soil (a type of loose soil) and can be easily disturbed by a slight continuous rainfall. Thus, sediments come along with the river discharge deposited in the low-lying areas, and therefore, the space occupied by the excess river water becomes smaller and then flood occurred. Such phenomenon create disturbance to the ecosystem of water resource management. There are several factors for soil erosion such as deforestation, human encroachment, climate change, and development activities. It is impossible to mitigate all these factors affecting to soil erosion, but it is always possible to minimize or slow down the process of soil erosion which includes monitoring the cutting down of trees and agricultural practices in hilly areas and application of strict laws for conservation of forest area while taking place of any developmental activities (road development, infrastructure planning, etc.). If these provisions are in place, flood occurrence can be minimized effectively.

1.9

Validation

The predicted (present study), simulated result (literature) and ground data (IFCD) were compared for the respected years (2015, 2017, 2019, and 2021). For the year 2015, the predicted area inundated by flood water is 39.4km2 with the simulated discharge of 2452.87 m3/s which is very high compared with the other years in both the cases (predicted and simulated). The collected ground data is also highest (2414.15 m3/s) in the year 2015 as compared with other years. Thus, this very high flood classification for the year 2015 is compared with the simulated SWAT model (Behera and Devi 2022) result and ground data, and it has been found out that they match the same result (high flood class) (Table 1.5).

1.10

Conclusion

The predicted flood affected is highest in the year 2015 followed by 2017, 2021, and 2019. Thus, it is concluded that throughout the year, this study region is affected by low to high flood intensity. Indeed MIF method can be effectively used for flood modeling apart from the common application in drought modeling. Therefore, further wide application of MIF method in flood modeling is encouraged so that its effectiveness could be improved. As the study region is in the valley plain part of the state which is frequently subjected to flooding, and as evidenced in recent years of flood events in the region, it is suggested that the agricultural activities as well as other infrastructural services should be well planned and monitored.

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Flood Modeling Using MIF Method with GIS Techniques: A Case Study of. . .

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Table 1.5 Flood class based on predicted, simulated, and observed data Validation remarks

Year 2013

Present study (high flood, km2) –

Predicted discharge (SWAT model)a (m3/s) 907.981

Observed discharge (IFCD)b (m3/s) 922.863

Present study (MIF model) –

2014

–

929.45

914.9501

–

2015

33.6

2452.87

2414.15

Very high flood

2017 2019 2021

32.5 22 23.6

– – –

– – –

High flood High flood High flood

SWAT modela No flood No flood Very high flood – – –

Ground datab No flood No flood Very high flood – – –

a

Predicted discharge (swat model), result taken from Behera and Devi (2022) Observed discharge (IFCD) or ground data, result collected from Irrigation and Flood Control Department (IFCD), Government of Manipur b

1.11

Future Scope

As GIS technology continues to advance and become more accessible, and with increasing concerns about climate change and flood hazards, the MIF method can provide valuable insights for flood mapping and mitigation efforts. The MIF method allows for the integration of multiple influencing factors, such as elevation, slope, land use, rainfall, and proximity to rivers or coastlines, which can result in more accurate flood mapping compared to single-factor methods. With the availability of high-resolution data and improved algorithms, the accuracy of flood mapping using the MIF method is expected to increase, aiding in better flood prediction and planning. As climate change continues to affect weather patterns and precipitation levels, flood risks are projected to increase in many regions. The MIF method can be used to assess the vulnerability of different areas to flooding under changing climate conditions. By incorporating climate change scenarios into the MIF method, it can help in identifying areas that may be more prone to flooding in the future, facilitating proactive measures for climate change adaptation and resilience planning. Therefore, the scope of the application of the MIF method can be achieved with one of the following objectives in future studies for flood modeling and assessment: 1. Integrated Decision Support System: The MIF method can be integrated into decision support systems that provide real-time flood monitoring and early warning systems. By combining MIF-based flood mapping with real-time data on precipitation, river levels, and weather forecasts, decision-makers can have access to up-to-date information for flood response and emergency management. This can enable more effective and timely decision-making in flood-prone areas.

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2. Urban Planning and Infrastructure Development: Urban areas are often at higher risk of flooding due to impervious surfaces and inadequate drainage systems. The MIF method can be used in urban planning and infrastructure development to identify flood-prone areas and guide the location of critical infrastructure such as roads, buildings, and utilities. This can help reduce the exposure and vulnerability of urban areas to floods and support sustainable urban development. 3. Community Engagement: The MIF method can involve local communities in flood mapping efforts, allowing them to contribute their knowledge and experience of local flood hazards. Community engagement can enhance the accuracy and relevance of flood mapping results and also raise awareness and understanding of flood risks among local residents. This can promote community resilience and preparedness and facilitate participatory decision-making in flood risk management. 4. Insurance and Risk Assessment: Flood mapping using the MIF method can support insurance companies and risk assessment agencies in determining flood risk zones and calculating insurance premiums. Accurate flood mapping can help in estimating potential losses due to floods, assisting in risk management, and underwriting decisions. Insurance companies can also use flood mapping results to promote risk reduction measures among policyholders, leading to more resilient communities.

References Ajin RS, Krishnamurthy RR, Jayaprakash M, Vinod PG (2013) Flood hazard assessment of Vamanapuram river basin, Kerala, India: An approach using remote sensing & GIS techniques. Adv Appl Sci Res 4(3):263–274 Anbarasu S, Brindha K, Elango L (2020) Multi-influencing factor method for delineation of groundwater potential zones using remote sensing and GIS techniques in the western part of Perambalur district, southern India. Earth Sci Inf 13:317–332. https://doi.org/10.1007/s12145019-00426-8 Behera PK, Devi TT (2022) Study on Impact of Urbanization by SWAT Model in Iril River, Northeast India. In: Jha R, Singh VP, Singh V, Roy LB, Thendiyath R (eds) Hydrological modeling, Water science and technology library, vol 109. Springer, Cham. https://doi.org/10. 1007/978-3-030-81358-1_29 Borah H, Deka S (2022) Exploration of potential zones of groundwater in Jamuna Watershed, Assam, by applying multi-influencing factor technique. J Indian Soc Remote Sens 51:75–91 Bronstert A, Agarwal A, Boessenkool B, Crisologo I, Fischer M, Heistermann M, Köhn-Reich L, López-Tarazón JA, Moran T, Ozturk U, Reinhardt-Imjela C (2018) Forensic hydrometeorological analysis of an extreme flash flood: The 2016-05-29 event in Braunsbach, SW Germany. Sci Total Environ 630:977–991 Danumah JH, Odai SN, Saley BM, Szarzynski J, Thiel M, Kwaku A, Kouame FK, Akpa LY (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire). Geoenvironmental Disasters 3(1) https://doi.org/10.1186/s40677-016-0044-y Das J, Umamahesh NV (2017) Uncertainty and nonstationarity in streamflow extremes under climate change scenarios over a river basin. J Hydrol Eng 22(10):04017042

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Das J, Umamahesh NV (2018) Assessment of uncertainty in estimating future flood return levels under climate change. Nat Hazards 93:109–124 Das J, Umamahesh NV (2022) Investigating risk, reliability and return period under the influence of large scale modes, and regional hydrological variability in hydrologic extremes. Hydrol Sci J 67(1):65–81 Dash P, Sar J (2020) Identification and validation of potential flood hazard areas using GIS-based multi-criteria analysis and satellite data-derived water index. J Flood Risk Manag 13(3):1–14 Hamdi SA, Ahmad SA, Saleh GJIA (2019). Analysis of basin geometry in Ataq Region, Part of Shabwah Yemen: using remote sensing and geographic information system techniques. Bull Pure and Appl Sci. 38 F (Geology), No.1, 2019: P.1–15, 2019 Hammami S, Dlala M, Zouhri L, Souissi D, Souei A, Zghibi A, Marzougui A (2019) Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia ). Arab J Geosci 12:1–16 Hauer C, Leitner P, Unfer G, Pulg U, Habersack H, Graf W (2018) The role of sediment and sediment dynamics in the aquatic environment. In: Riverine ecosystem management. Springer, Cham. https://doi.org/10.1007/978-3-319-73250-3_8 IMD (2022) Statewise rainfall information, a web report. Indian Meteorological Department Jarajapu DC, Rathinasamy M, Agarwal A, Bronstert A (2022) Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization. J Hydrol 613:128341 Jonkman SN, Dawson RJ (2012) Issues and challenges in flood risk management—editorial for the special issue on flood risk management. Water 2012(4):785–792 Khan SI, Hong Y, Wang J, Yilmaz KK, Gourley JJ, Adler RF, Brakenridge GR, Policelli F, Habib S, Irwin D (2011) Satellite remote sensing and hydrologic modeling for flood inundation mapping in lake Victoria basin: implications for hydrologic prediction in ungauged basins. IEEE Trans Geosci Remote Sens 49:85–95 Kondolf GM, Gao Y, Annandale GW, Morris GL, Jiang E, Zhang J, Cao Y, Carling P, Fu K, Guo Q, Hotchkiss R, Peteuil C, Sumi T, Wang HW, Wang Z, Wei Z, Wu B, Wu C, Yang CT (2014) Sustainable sediment management in reservoirs and regulated rivers: experiences from five continents. Erath’s Future 2(5):256–280. https://doi.org/10.1002/2013EF000184 Lechowska E (2018) What determines flood risk perception? A review of factors of flood risk perception and relations between its basic elements. Nat Hazards 94:1341–1366. https://doi.org/ 10.1007/s11069-018-3480-z Magesh NS, Chandrasekar N, Soundranagam JP (2012) Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci Front 3(2):189–196 Mangukiya NK, Sharma A (2022) Flood risk mapping for the lower Narmada basin in India: a machine learning and IoT-based framework. Nat Hazards 113(2):1285–1304 Mitra R, Saha P, Das J (2022) Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India. Geomat Nat Haz Risk 13(1):2183–2226. https://doi.org/10.1080/19475705.2022.2112094 Munawar HS, Hammad AWA, Waller ST (2022) Remote sensing methods for flood prediction: a review. Sensors (Basel) 22(3):960 Nsangou D, Amidou K, Zakari M, Ngoupayou JRN (2022) The Mfoundi watershed at yaoundé in the humid tropical zone of Cameroon: a case study of urban flood susceptibility mapping. Earth Systems and Environment 6(2):99–120. https://doi.org/10.1007/s41748-021-00276-9 Ouma OY, Tateishi R (2014) Urban flood vulnerability and risk mapping using integrated multiparametric AHP and GIS: methodological overview and case study assessment. Water 6(6): 1515–1545 Pandey P, Tiwari SK, Pandey HK, Chaurasia AK, Singh S (2021) Identification of potential recharge zones in drought prone area of bundelkhand region, India, Using SCS-CN and MIF technique under GIS-frame work. Water Conserv Sci Eng 6:105–125

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Saikumar G, Pandey M, Dikshit PKS (2022) Natural river hazards: their impacts and mitigation techniques. In: River dynamics and flood hazards: studies on risk and mitigation. Springer, Singapore, pp 3–16 Senan CPC, Ajin RS, Danumah JH, Costache R, Arabameri A, Rajaneesh A, Sajinkumar KS, Kuriakose SL (2023) Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models. Stoch Env Res Risk A 37(2):527–556. https://doi.org/ 10.1007/s00477-022-02267-2 Singh, NM, Devi TT (2022) Assessment and Identification of drought prone zone in a Low Laying Area by AHP and MIF method: A GIS based study, IOP Conference Series: Earth and Environmental Science, 1084 012047. https://doi.org/10.1088/1755-1315/1084/1/012047 Singh L, Saravanan S, Jennifer JJ, Abijith D (2021) Application of multi-influence factor (MIF) technique for the identification of suitable sites for urban settlement in Tiruchirappalli City, Tamil Nadu, India. Asia-Pac J Reg Sci 5(3):797–823. https://doi.org/10.1007/s41685-02100194-8 Sinha R, Bapalu GVS, L.K. and Rath, B. (2008) Flood risk analysis in the Kosi River basin, north Bihar using the multi-parametric approach of analytical hierarchy process (AHP). J Indian Soc Remote Sens 36(4):335–349 Sinha J, Das J, Jha S, Goyal MK (2020) Analysing model disparity in diagnosing the climatic and human stresses on runoff variability over India. J Hydrol 581:124407 Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlala M (2020) GIS-based MCDM– AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int 35(9):991–1017. https://doi.org/10.1080/10106049.2019.1566405 Taheri K, Missimer TM, Taheri M, Moayedi H, Mohseni PF (2020) Critical zone assessments of an alluvial aquifer system using the multi-influencing factor (MIF) and analytical hierarchy process (AHP) models in western Iran. Nat Resour Res 29(2):1163–1191. https://doi.org/10.1007/ s11053-019-09516-2

Chapter 2

A Case Study on Estimating the Ecosystem Service Values (ESVs) Under Anthropogenic Influences for Chennai and Hyderabad Sudardeva and Manali Pal

Abstract Ecosystem services are inevitable to all biota on the earth and possess a value of approximately 125 trillion USD. Although the complete valuation of ecosystem services in monetary terms is uncertain, they are sine qua non to frame policies regarding the utilization of resources in a sustainable way. Moreover, the continuous urban growth imparts variations to the urban ecological land use and land cover (LULC) and urban ecosystem functions that possess serious challenges. However, studies on quantifying ecosystem services and assessing them under anthropogenic influence are scarce, especially for the metropolitans in India. In this scenario, we selected Chennai metropolitan area (CMA) and the Greater Hyderabad Municipal Corporation (GHMC), two rapidly urbanizing metropolitan areas with increasing anthropogenic activities observed since last decade, for quantifying the ecosystem service value (ESV). The study applied the approach proposed by Costanza R et al. (Nature, 1997;387:253–260) that uses the spatiotemporal variations of LULC to compute the ESV. The LANDSAT data products are used to generate LULC for the CMA and GHMC for each decade since 1995–2022 (to mark the economic transition for the country), for example, for the years 1995, 2005, 2015 and 2022. The study reveals the drastic changes in the area of individual classes. Vegetation has shrunken noticeably between 1995 and 2022, followed by waterbodies for both the areas. Due to urbanization, the builtup is found to be increased in an unregulated way that reduces ESV. The substantial loss in ESV questions the resilience of the study areas, and this trend continues till the end of the observation period. The findings summarize the loss in ecosystem services that need urgent measures to be taken to enhance the urban ecosystem sustainability through the restoration of waterbodies and effective land management practices. Keywords Ecosystem services · Ecosystem functions · Ecosystem service values · Anthropogenic activities · Urbanization Sudardeva · M. Pal (✉) Department of Civil Engineering, NIT, Warangal, Telangana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Pandey et al. (eds.), River, Sediment and Hydrological Extremes: Causes, Impacts and Management, Disaster Resilience and Green Growth, https://doi.org/10.1007/978-981-99-4811-6_2

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Introduction

The ecosystem is a biophysical system, defined by the complex interaction of biotic and abiotic components in a physical environment to form a new bubble in life form. The interdependency of biotic components among themselves and with the abiotic components defines the behaviour of a biological organism in the ecosystem. The behaviour of these components results in ecosystem functioning through which ecosystem services are derived in the form of goods and services. These ecosystem services (ES) are inevitable for the existence of life forms; they are direct and indirect benefits drawn from nature and enjoyed by humans for their livelihood. Even though all biotic components influence ecosystem functioning, human beings influence the system on a greater scale due to the large-scale utilization of services offered by the ecosystem. Hence, the quantification of the association between rapid urban growth and ecosystem services plays an essential role for the urban sustainability and development related to planning and policies. A study by Costanza et al. (1997) listed 17 ecosystem services that are derived from a single or a combination of two or more ecosystem functions. Another study by Rudolf S. de Groot categorized ecosystem functions into the regulation function (maintenance of essential ecological process and life support system), habitat functions (providing habitat for plants and animal species), production functions (provision of natural resources) and information functions (providing opportunities for evolution). The definitions of individual ecosystem services offered under each categorization are as follows: 1. Regulation functions: Those functions which regulate the essential ecosystem process through which basic life system is supported are termed as regulation functions. The services that are derived from bio-geochemical cycles and other bio-spheric processes to the ecosystem include gas regulation, climate regulation, disturbance prevention, water regulation, water supply, soil formation, soil retention, nutrient regulation, waste treatment, pollination and biological control. 2. Habitat functions: The ecosystem services that enable the life forms to get habituated with normal living in order to reproduce and proliferate among themselves such that there is a conservation in biodiversity and genetic diversity are known to as habitat functions. 3. Production functions: The livelihood of human is supported by intake of biomass which is a direct result of synthesis from primary producers and secondary producers. They include food, raw materials, genetic resources, medicinal resources and ornamental resources. 4. Information functions: These functions provide essential reference function that is transferred as information to humans in the process of evolution. The services generated provide values to life and help us in understanding the need for existence of ecosystem. They include recreation, aesthetic information, cultural and artistic information, spiritual and historic information, science and education. The above listed services may not be able to catalogue all the ecosystem services, since there are a lot of unidentified benefits used by human in the ecosystem. The

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dynamics of ecosystem along with the human influence urges us to look into the idea of ecosystem sustainability. To implement them in the society, the resources and services availed so far have to be utilized in a sustainable manner. There is a need for an account of the ecosystem services in terms of monetary value for prioritising human made decisions over development and conservation. The value of the world’s ecosystem services and natural capital are estimated based on willingness to pay by the resource utilizers (Costanza et al. 1997, 2008, 2014). The study by De Groot et al. (2002, 2010, 2012) and by Kreuter et al. (2001), Liu et al. (2012) created ecosystem service valuation database by analysing the previous work on the valuation in monetary terms. The values of ecosystem services are updated from his earlier work by Costanza et al. (2011), considering consumer price index, and provided the factor of conversion as 1.38 from the year 1997 to 2011. A limited number of studies have been found on this problem statement, particularly for India, and the examples are as follows: Das and Das (2019) studied the impacts of urbanization and its dynamics on ecosystem services for Malda town in West Bengal, India. Sannigrahi et al. (2019) assessed the spatiotemporal variation in ESV for a natural reserve Sundarbans region, derived from spatiotemporal data of land use and land cover (LULC). Hence the valuation of ecosystem services depends on LULC Sannigrahi et al. (2020a, 2020b), and Sannigrahi et al. (2017, 2019) compared different supervised machine learning classification techniques to derive ecosystem service values (ESVs). The study aims to present the trend in LULC of each category for a period of four decades (1995–2022) since the introduction of major economic reforms (1991) in India (Vikramani 2006). The change in the area occupied by each ecosystem class significantly impacts the functioning of the ecosystem, which further affects the extraction of ecosystem services. The more the natural element persists in the ecosystem, the least it is disturbed. But the needs of humans heavily influence LULC. It is essential to view and plan developmental initiatives in a sustainable way that alters the ecosystem of an urban area such that it should be a balance between resource utilization and extraction of ecosystem services. The study will state the current state of ecosystem functions and ESV derived from LULC. It will aid us to formulate indices that will help the local government to monitor and frame policies to achieve Sustainable Development Goal (SDG) concerning the urban area to make cities and human settlements inclusive, safe, resilient and sustainable. Furthermore, the change in LULC deteriorates the ecosystem and makes them prone to hazards. Since the increase in builtup area is associated with the increase in population, which in turn results in increase in demand of resources of water and energy, this additional demand in the existing system increases the stress on groundwater through extensive drafting, thus resulting in reduced baseflow component of the stream and may disturb the perennial nature of the stream. Additionally, the relationship between builtup and impervious surfaces is related to the increase in flood scenarios and land surface temperature (LST) that can affect the hydrological extremes. The valuation of ES would help in framing policy in an urban area for sustainable land use management practices. However, as mentioned earlier, the studies concerning the assessment of ES for urban region in India are limited.

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Hence, this study has taken up to estimate the ESV for two metropolitans, namely, the Greater Hyderabad Municipal Corporation (GHMC) and Chennai Metropolitan Area (CMA) which has undergone rapid unregulated urbanization. The major objectives of the study are as follows: 1. To investigate the changes in LULC due to urbanization for the study area, that is, GHMC and CMA, over the last two decades, that is, 1995–2022 2. To estimate and detect the trends in ESVs offered by individual LULC classes and individual ecosystem functions

2.2

Rational of the Study

Industrialization and its associated urbanization have made lives of human being more comfortable than ever before, and they significantly impacted human way of consuming and utilizing resources. This anthropogenic utilization has brought severe degradation of the ecosystem and reduced the services offered by the ecosystem (Chopra et al. 2022). Since this is the age of sustainable development, a prior knowledge on services offered by the ecosystem is needed to draft policies for optimal and environment-friendly utilization of resources. This study attempts to estimate the ecosystem service values of two Indian metropolises and analyse the trend in the services between two decades, that is, 1995–2022. It also attempts to highlight the impacts of change in LULC triggered by economic opportunities on ecosystem service values.

2.3

Limitation of the Study

Valuation of ecosystem services involves the process of identifying the ecosystem services and assigning a monetary value to them. This study considers only a list of 17 ecosystem services based on a study performed by Costanza et al. (1997). The study excludes some of the services from the process limiting the idea of valuing the entire ecosystem. The values of ecosystem services are considered in terms of US Dollar (USD) and are not converted to Indian Rupee (INR) according to the present consumer price index.

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Materials and Methods

2.4.1

Study Area

2.4.1.1

Greater Hyderabad Municipal Corporation

Hyderabad is the capital city and most populous city in the Indian state of Telangana. The Greater Hyderabad Municipal Corporation (GHMC) is an urban conglomeration comprising of two main cities, that is, Hyderabad and Secunderabad, along with its suburbs shown in Fig. 2.1. The study area is holding 9.7 million human habitants and spreads over an area of 550 km2, which is further divided into 6 zones and 30 circles for administration convenience. It extends between 17°20′N to 17°60′N latitude and 78°23′E to 78°68′E longitude and receives an average precipitation of 840mm (Agilan et al. 2015). The river Musi which originates in Vikrabad flows in the city that acts as natural carrier to drain the water. The GHMC experiences an arid climate with mean monthly temperature varying 22.6°C in January and 32.3°C in May (Warrier et al. 2011). The average elevation is 580m above mean sea level and is located in Deccan plateau. The study area serves as an industrial hub for pharma and life sciences, food processing and service sector, since it is stressed with 27% of state’s population living in 0.8% of state’s area. It shares almost half (43.5%) of total gross state domestic product (GSDP) of Telangana. The economic growth attracted population inside and outside the state of Telangana that resulted in unregulated land use practices.

2.4.1.2

Chennai Metropolitan Area

Chennai is one of the oldest municipal corporations in the world and serves as the capital of the Indian state of Tamil Nadu. The Chennai Metropolitan Area (CMA) comprises Greater Chennai Corporation, Avadi Corporation, Tambaram Corporation and its suburbs, which accounts for 12 million people spread over 1182 km2 and

Fig. 2.1 Location of study area – (a) GHMC and (b) CMA

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Fig. 2.2 Trend in LULC variation for GHMC from 1995 to 2022

shown in Fig. 2.2. The study area experiences a tropical wet and dry climate and is highly humid because of its location. It receives rainfall on an average of about 1400 mm where 60% of it occurs between October and December, that is, due to northeast monsoon (Devi et al. 2019). Three rivers Cooum, Adayar and Kosasthailayar flow in the study area and drains into the Bay of Bengal. Buckingham Canal once served as a navigational channel runs parallel to the coast. Heavy industrialization and urbanization in post-independence period sound the need for ecosystem valuation.

2.4.2

Data Source and LULC Classification

The estimation of ESVs depends on the accurate measurement of proportionate area that falls under each ecoregion, since an ecoregion offers unique ecosystem services. Remote sensing data products are reliable source for quantifying area under each ecoregion. For the study, geometrically and radiometrically calibrated Landsat data are obtained from the US Geological Survey (USGS) Earth Explorer that is for different time periods. The detail description of Landsat data used in the study is given in Table 2.1. The data for the month of January (with cloud cover less than 10%) are considered for the analysis for the years 1995, 2005, 2015 and 2022, in order to eliminate the seasonal variation and to maintain consistency in classification. The data are classified into five LULC classes, that is, waterbodies, vegetation, builtup, cropland and barrenland, using a supervised machine learning algorithm, namely, support vector machine (SVM). The LULC classification using SVM has produced better accuracy (Sannigrahi et al. 2019) comparing with other techniques

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Table 2.1 Description of the record of Landsat data for different years used in the study Year 1995 2005 2015 2022

Data Landsat 5 Landsat 5 Landsat 8 Landsat 9

Sensor TM TM OLI OLI

Month of image acquisition January 1995 January 2005 January 2015 January 2022

Spatial resolution (m) 30 30 30 30

like Artificial Neural Network, Decision Tree and Maximum Likelihood Classification. Google Earth Engine is used to classify the images, since it reduces the tedious task of data handling. The cloud-based setup helps us to derive and store the output without need for handling many temporary data. The platform’s code editor is used to obtain the Landsat data and filtered for minimal cloud cover. An average of 200 training point is considered for individual class, that is, waterbodies, cropland, vegetation, builtup and barrenland, and Support Vector Machine (SVM) classifier (libsvm) is used for the supervised classification. The classified data for the years of 1995, 2005, 2015 and 2022 are used to obtain the proportionate area under each class and are analysed for spatiotemporal variations in an individual classed over the time period of study. The year 1995 was taken as reference period to define the variation in LULC classes and estimated as follows: △LULCi =

LULCfinal - LULCinitial × 100 LULCinitial

where △LULCi is the change in an individual LULC observed during the time frame and LULCfinal and LULCinitial is the particular LULC unit at the beginning and ending of the study. The average overall accuracy assessment of supervised classification for the study areas of CMA and GHMC is 73% and 55%, respectively. The accuracy can be enhanced by a more apparent distinction between cropland and barrenland, as non-cultivable areas are classified as barrenland. The flaws in the classification limit the study.

2.4.3

Estimation of Ecosystem Service Values

Ecosystem service value is estimated as the summation of product of the ecosystem coefficient (for LULC classes and ecosystem services) with proportionate area of LULC classes. We considered 17 ecosystem services offered by individual LULC classes and the value for ecosystem service coefficient obtained from Sannigrahi et al. (2019) are listed in Table 2.2. And the ESV can be mathematically computed with the following equation:

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Table 2.2 Ecosystem service value for different LULC classes in US$/ha/year Ecosystem services Gas regulation Climate Regulation Disturbance Regulation Water regulation Water supply Erosion control Soil formation Nutrient cycling Waste treatment Pollination Biological control Habitat service Food production Raw material Genetic service Recreation Cultural service

Waterbodies 0 13 81 166 12 88 0 51 82 0 26 66 17 15 3 60 54

Vegetation 0 6 0 0 10 8 0 0 11 5 5 184 181 8 184 4 25

Cropland 0 215 0 0 234 74 370 0 208 12 17 0 1269 115 546 43 0

Barrenland 10.06 0 0 15.1 0 10.06 0 0 50.32 0 0 10.06 0 0 0 0 5.03

Builtup 0 26 0 1 0 0 0 0 0 0 0 0 0 0 0 161 0

17

ESVi =

aV ck × Ai j=1

where ESVi is the ecosystem service value offered by an individual class and Vck is the ecosystem service coefficient for individual ecosystem service in US$/ ha/year: 5

ESVtotal =

aESVi i=1

ESVtotal in a year is the total ecosystem service value offered by all individual LULC classes from their services. The change in ESV (△ESVi) is calculated as follows: △ESVi =

ESVfinal - ESVinitial × 100 ESVinitial

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Results and Discussion

2.5.1

Classification and Spatiotemporal Changes of LULC

2.5.1.1

GHMC

In the year 1995, the LULC class vegetation and builtup are found to be the predominant land cover category (46.5% and 46%) followed by waterbodies (4%), and then cropland and barrenland are the least ones. The dominance of builtup in LULC is found in 2005, and vegetation class is found to have considerable loss in proportion of area as shown in Fig. 2.2. During the years 1995–2022, builtup area has increased 33% at the cost of vegetation and waterbodies changing the study area landscape with builtup as the dominant category. The spatiotemporal changes in the study area are presented in Fig. 2.3. Even though the reduction in area of waterbody is observed, the rate of decrease in the area from 2015 to 2022 is very low compared to the rate of decrease from 2005 to 2015. The area under waterbodies, vegetation and cropland has shrunken to 36.05%, 38.69% and 75.13%, respectively, during the period of study between 1995 and 2022. Meanwhile the area under builtup and barrenland are found to increase 32.98% and 218.02% during the study. The decade of 2005–2015 has witnessed sharp transition in area under each LULC class, that is, the area under waterbodies, cropland and vegetation has been transformed into other LULC classes.

2.5.1.2

CMA

The Chennai Metropolitan Area had builtup as the dominant land category occupying 32.48% of total area followed by cropland, vegetation, barrenland and then waterbodies (29.62%, 14.91%, 13% and 10.14%), respectively, in 1995. The areas under builtup and barrenland are found to increase during the study period of 1995–2022 amounted to 36.46% and 42%, respectively, as seen in Fig. 2.4. Vegetation, cropland and waterbodies are found to follow decreasing trends throughout the study period, and estimated percentage losses in the area compared with the reference year are 24.8%, 30% and 45.96%, respectively. The increasing area under builtup and barrenland could be attributed with the decrease in the area associated with the LULC class of vegetation, cropland and waterbodies (Fig. 2.5).

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Fig. 2.3 Spatiotemporal variation of LULC observed in GHMC (1995–2022)

2.5.2

Ecosystem Service Values

2.5.2.1

GHMC

Figure 2.6 depicts the ESV derived from each LULC in GHMC. Waterbodies provide 14 ecosystem services out of 17 considered for this study, and it contributes to 1.8 million US Dollars (USD) in the year 1995 (Costanza et al. 1997) and gradually reduced to 1.7 million USD in 2005, 1.8 million USD in 2015 and 1.14 million USD in 2022. Vegetation provides 12 ecosystem services and contributed 18.16 million USD in the year 1995. It is the highest contributed LUCC class to ESV and then decreasing throughout the study period as 16.95 million USD in 2005,

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Fig. 2.4 Trend in LULC variation for CMA from 1995 to 2022

14.31 million USD in 2015 and 10.23 million USD in 2022 indicating the rate of decrease in ESV increasing as the time progressed. Climate regulation, water regulation and recreation are the services provided by the builtup class. The valuation of these services is estimated around 4.5 million USD in 1995 and found to be a decreased till the end of the study period (4.9 million USD in 2005, 5.88 million USD in 2015 and 7.1 million USD in 2022). The rate of increase in ESV was found to be increased during later time period of study (2005–2015, 2015–2022). Though builtup is a dominant land use category, the net ecosystem services derived from it were less compared with another existing natural ecosystem. From this study, it is evident that there is a linkage between LULC and ecosystem service values. The reduction in the area of waterbodies (37.67%) has reflected in ESV, that is, the share in total ESV dropped from 6.45% in 1995 to 5.73% in 2022. Vegetation has drastically minimized in area by 43.6% where almost half of the area are converted into other eco-class. The contribution of vegetation to ESV dropped from 74% to 55% indicating the overall loss to the study area. The ecosystem services provided by vegetation have been following a decreasing trend throughout the period. The area under builtup found to be increased 33% during the period 1995–2022, the share to the total area calculated as 46.13% in 1995, 55.9% in 2005, 57.8% in 2015 and 61.8% in 2022. The ESVtotal is estimated 28.87 million USD in the year 1995 and 18.49 million USD in the year 2022 showing decreasing trend with an overall loss of 28.52% (10.38 million USD). The primary contribution of ESV is derived from vegetation followed by builtup, cropland and waterbodies, and this order continued even though there is net decrease in contribution from the individual classes except builtup. Production functions contribute higher share in ESV followed by information functions, habitat functions and regulation functions throughout the study as

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Fig. 2.5 Spatiotemporal variation of LULC observed in CMA (1995–2022)

shown in Fig. 2.7. The fivefold increase of area in barrenland observed between 2005 and 2015 has found to be decreased 20% during 2015–2022, along with the increase in builtup. This could be reasoned as the barrenland serves as an intermediate transition class towards the conversion of vegetation and cropland into builtup.

2.5.2.2

CMA

Cropland holds the highest share in contributing services to the ecosystem and is estimated to be 109 million USD in the year 1995, 94.9 million USD in 2005, 87.5 million USD in 2015 and 76.5 million USD in 2022 as shown in Fig. 2.8. Though cropland shares higher ESV among other eco-classes, there is constant decrease in

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Fig. 2.6 The ESVs derived from LULC for GHMC

Fig. 2.7 The ESV derived from ecosystem functions for GHMC

ESV (30%) during the study period. The increase in ESV derived from the builtup does not significantly increase the total ESV as shown in Fig. 2.8. This is a result of significant reduction in the area occupied by the cropland. Vegetation is the next highest contributor of ES in the CMA accounting 11.18 million USD, 10.56 million USD, 9.4 million USD and 8.4 million USD, and this eco-class too followed the decreasing trend in ESV as cropland during the study. Contradictorily, ESVs derived from builtup on the barrenland are found to be increasing throughout the study. This is reflected from decreasing trend in the area

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Fig. 2.8 The ESV derived from LULC class for CMA

occupied by the waterbodies and increasing trend in area occupied by the buildup and barrenland during the study. The ESV for the CMA is estimated as 138.03 million USD in the year 1995 and found decreased as 123 million USD in 2005, 112.91 million USD in 2015 and then 101 million USD in 2022. The loss incurred in ESV amounts during the study of 36.24 million USD (26.25%) is attributed to the increase in the builtup and barrenland whose ecosystem services contribution is minimum. Figure 2.9 clearly shows that the production function dominates other ecosystem functions in generating ESV but follows decreasing trend throughout the study. This trend is followed by regulation functions and habitat functions except information function, where ESV contribution is found with increasing trend throughout the study. Regulation functions that regulate and maintain the ecosystem through its services (climate regulation, water regulation and supply, disturbance regulation and waste Treatment) are found to have subsequent decrease in generation of ESVs between 1995 and 2022. This fact questions the resilience of the study area in climate changeinduced extremes. The decrease in regulation function indicates loss in basic life support services derived from the ecosystem.

2.6

Conclusion

In this study, we have calculated the spatiotemporal ESVs of Greater Hyderabad Municipal Corporation (GHMC) and Chennai Metropolitan Area (CMA) from the different LULC data for the period 1995–2022. The monetary values for

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Fig. 2.9 The ESV derived from ecosystem functions for CMA

17 ecosystem services are calculated using ecosystem service coefficient values obtained from Sannigrahi et al. (2019). At instant builtup is the dominant land use category in the study, followed by vegetation and waterbodies, respectively. Due to the expansions in builtup, the ecosystem services such as climate regulation and recreation have increased during 1995–2022, whereas the decrease in ESV such as water regulation, water supply, erosion control, nutrient cycling, raw material and genetic services is observed due to shrinkage in the area under waterbodies and vegetation. It also describes the impact of human interference on ecosystem and natural capital. The study reveals that the area of our interest has undergone unregulated land use practices due to anthropogenic activities, since builtup is the only class found increased throughout the period of study. The study concludes that anthropogenic activities on LULC have severe impact on ecosystem functions and its derived ecosystem services. It is observed that years between 2005 and 2015 witnessed sharp transition in LULC and it could be reasoned with flowering economic activities. However, the reason has to be validated with further studies. The change detection analysis study on LULC would be able to quantify the transformation of one class into another and it limits the present study. Lastly, the study points that there is an overall loss in ESV derived from ecosystem services due to anthropogenic activities, 6 (clean water and sanitation, good health and well-being, sustainable cities and communities, climate action, life below water, life on land) out of 17 Sustainable Development Goals are more affected. The result of this study would be helpful in understanding the LULC dynamics and its associated changes in the ESV values to frame policies to attain ecosystem sustainability.

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References Agilan V, Umamahesh NV (2015). Detection and attribution of non-stationarity in intensity and frequency of daily and 4-h extreme rainfall of Hyderabad, India. J Hydrol, 530, 677–697. https:// doi.org/10.1016/j.jhydrol.2015.10.028 Chopra B, Khuman YSC, Dhyani S (2022) Advances in ecosystem services valuation studies in India: learnings from a systematic review. Anthropocene Sci 1(3):342–357. https://doi.org/10. 1007/s44177-022-00034-0 Costanza R, d’Arge R, De Groot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S, O’neill RV, Paruelo J, Raskin RG (1997) The value of the world's ecosystem services and natural capital. Nature 387(6630):253–260. https://doi.org/10.1038/387253a0 Costanza R, Pérez-Maqueo O, Martinez ML, Sutton P, Anderson SJ, Mulder K (2008) The value of coastal wetlands for hurricane protection. Ambio:241–248. https://doi.org/10.1016/j.gloenvcha. 2021.102328 Costanza R, Kubiszewski I, Ervin D, Bluffstone R, Boyd J, Brown D et al (2011) Valuing ecological systems and services. F1000 biology reports 3. https://doi.org/10.3410/B3-14 Costanza R, De Groot R, Sutton P, Van der Ploeg S, Anderson SJ, Kubiszewski I, Farber S, Turner RK (2014) Changes in the global value of ecosystem services. Glob Environ Chang 26:152– 158. https://doi.org/10.1016/j.gloenvcha.2014.04.002 Das M, Das A (2019) Dynamics of urbanization and its impact on urban ecosystem services (UESs): a study of a medium size town of West Bengal, Eastern India. J Urban Manag 8(3): 420–434. https://doi.org/10.1016/j.jum.2019.03.002 De Groot RS, Wilson MA, Boumans RM (2002) A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecol Econ 41(3):393–408. https://doi.org/ 10.1016/S0921-8009(02)00089-7 De Groot RS, Alkemade R, Braat L, Hein L, Willemen L (2010) Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol Complex 7(3):260–272. https://doi.org/10.1016/j.ecocom.2009.10.006 De Groot R, Brander L, Van Der Ploeg S, Costanza R, Bernard F, Braat L, Christie M, Crossman N, Ghermandi A, Hein L, Hussain S (2012) Global estimates of the value of ecosystems and their services in monetary units. Ecosyst Serv 1(1):50–61. https://doi.org/10.1016/j.ecoser.2012. 07.005 Devi NN, Sridharan B, Kuiry SN (2019). Impact of urban sprawl on future flooding in Chennai city, India. J Hydrol, 574, 486–496. https://doi.org/10.1016/j.jhydrol.2019.04.041 Kreuter UP, Harris HG, Matlock MD, Lacey RE (2001) Change in ecosystem service values in the San Antonio area, Texas. Ecol Econ 39(3):333–346. https://doi.org/10.1016/S0921-8009(01) 00250-6 Liu Y, Li J, Zhang H (2012) An ecosystem service valuation of land use change in Taiyuan City, China. Ecol Model 225:127–132. https://doi.org/10.1016/j.ecolmodel.2011.11.017 Sannigrahi S, Rahmat S, Chakraborti S, Bhatt S, Jha S (2017) Changing dynamics of urban biophysical composition and its impact on urban heat Island intensity and thermal characteristics: the case of Hyderabad City, India. Model Earth Syst Environ 3:647–667. https://doi.org/10. 1016/j.jenvman.2019.04.095 Sannigrahi S, Chakraborti S, Joshi PK, Keesstra S, Sen S, Paul SK, Kreuter U, Sutton PC, Jha S, Dang KB (2019) Ecosystem service value assessment of a natural reserve region for strengthening protection and conservation. J Environ Manag 244:208–227. https://doi.org/10.1016/j. jenvman.2019.04.095 Sannigrahi S, Zhang Q, Joshi PK, Sutton PC, Keesstra S, Roy PS, Pilla F, Basu B, Wang Y, Jha S, Paul SK (2020a) Examining effects of climate change and land use dynamic on biophysical and economic values of ecosystem services of a natural reserve region. J Clean Prod 257:120424. https://doi.org/10.1016/j.jclepro.2020.120424 Sannigrahi S, Zhang Q, Pilla F, Joshi PK, Basu B, Keesstra S, Roy PS, Wang Y, Sutton PC, Chakraborti S, Paul SK (2020b) Responses of ecosystem services to natural and anthropogenic

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forcings: a spatial regression based assessment in the world's largest mangrove ecosystem. Sci Total Environ 715:137004. https://doi.org/10.1016/j.scitotenv.2020.137004 Vikramani A (2006) India's economic growth history: fluctuations, trends, break points and phases. Indian Econ Rev 41:81–103. https://www.jstor.org/stable/29793855 Warrier CU, Babu MP (2011) A comparative study on isotopic composition of precipitation in wet tropic and semi-arid stations across southern India. J Earth Syst Sci 120, 1085–1094. https://doi. org/10.1007/s12040-011-0121-2

Chapter 3

Groundwater Vulnerability Mapping Using Modified DRASTIC Model: A GIS-Based Case Study of Imphal East District, Manipur, India Haobam Bidyapati and Thiyam Tamphasana Devi

Abstract In this study, modified DRASTIC models (DRASTIC_AGRI and DRASTIC_LU models, AGRI stands for agriculture and LU stands for landuse) with GIS (Geographical Information System) tools were used to evaluate the vulnerability of groundwater (contamination and quality) in Imphal East District, Manipur, India, for the recent period (2021–2022). For the DRASTIC_AGRI model, seven input parameters (depth of water, net recharge, types of aquifer media, types of soil media, topographical slope, impact of the vadose zone and hydraulic conductivity) were used, and additionally, one more parameter (land use/land cover) is used in the DRASTIC_LU model. Out of these parameters (except hydraulic conductivity), data were collected from different government organisations, and for hydraulic conductivity, field measurements were performed using a field instrument (a Mini Disc Infiltrometer) at different locations within the study area. In order to construct the modified DRASTIC indexes, weights (1–5) and ratings (1–10) are assigned to these input parameters, and finally, using weighted overlay analysis, the indexes are derived. A Delphi method (Aller et al. 1985) is used to determine these ratings and weights. The results reveal that the vulnerability index of Imphal East ranges from 130 to 173 (DRASTIC_AGRI) and 120–182 (DRASTIC_LU), which indicates moderate to high vulnerability to groundwater contamination. The predicted index values are validated (R2 = 0.948, R2 = 0.934) using TDS (total dissolved solids), which is one of the major contributing parameters to groundwater pollution. It is concluded that even though the study region is not industrialised, urbanisation and human activities (including the use of pesticides in agricultural practises) may escalate the contamination of groundwater in this region, which needs to be checked regularly as well as widely to reduce the contamination further.

H. Bidyapati · T. T. Devi (✉) Department of Civil Engineering, National Institute of Technology, Imphal, Manipur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Pandey et al. (eds.), River, Sediment and Hydrological Extremes: Causes, Impacts and Management, Disaster Resilience and Green Growth, https://doi.org/10.1007/978-981-99-4811-6_3

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Keywords Groundwater quality · DRASTIC model · GIS techniques · Satellite data · Imphal East district Highlights • First study on the application of DRASTIC_AGRI model and DRASTIC_LU model with GIS techniques for groundwater vulnerability mapping in the study region and very few in entire North East India

3.1

Introduction

The relationship between rivers, sediment, hydrological extremes and groundwater is complex and interconnected (Gupta et al. 2023). The ecosystem of water resources is disturbed by several factors, and its two major extreme events (floods and droughts) are significant. River plays a major role for source of water as well as sweeping away the overflow discharge of the catchment which controls and reduces the amount of flood in the region (Saikumar et al. 2022). Groundwater can play a critical role in maintaining river flow during periods of drought, as it can act as a natural storage reservoir and slowly release water into the river over time (Saikumar et al. 2022). However, excessive sediment accumulation in the riverbed can reduce the rate of groundwater recharge and lead to a decline in water table levels, which can in turn exacerbate drought conditions (Saikumar et al. 2022). Hydrological extremes, such as floods, can also impact groundwater quality by introducing contaminants into the subsurface, which can then migrate and impact nearby drinking water wells (Gupta et al. 2023). Effective management of river sediment and hydrological extremes can help to maintain healthy groundwater resources by reducing sedimentation and the risk of groundwater contamination (Saikumar et al. 2022). Additionally, groundwater management strategies, such as recharge wells and aquifer storage and recovery, can help to increase groundwater storage and recharge rates, providing a buffer against hydrological extremes and supporting healthy river ecosystems (Gupta et al. 2023). In case of drought (scarcity or non-availability of surface water), the main source of water is groundwater (Singh and Devi 2022). Due to natural (laterals seepage of polluted water, siltation and sedimentation, etc.) as well as anthropogenic activities, that is , the use of pesticides in agricultural practice (Farooqi et al. 2009; Urseler et al. 2022) and in animal farming, conversion of native land to agriculture (Novotny 1999) and dumping of sewage on the ground without treatment (Reynolds et al. 2007; Dregulo and Bobylev 2021), groundwater is polluted. Siltation and sedimentation of river caused by soil erosion and loss lead to contamination of groundwater (Pal et al. 2022; Raju 2022; Wang et al. 2022). Various chemicals (pharmaceuticals and personal care products) present and flows along with the sediment are potential risks to groundwater and eventually to human health and aquatic life (Xie et al. 2022). Thus, assessment on groundwater vulnerability to contamination and pollution becomes an essential part

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for monitoring as well as for preventive measures to the groundwater pollution. The use of satellite data with GIS (Geospatial Information System) techniques is a wellknown approach for modelling the groundwater vulnerability (Indulekha et al. 2019; Ahirwar et al. 2022). And one the commonly used GIS based model is DRASTIC (each letter represents the major factors affecting the transfer of contamination) for modelling of groundwater vulnerability (first time introduced by Aller et al. (1985), Ersoy and Gultekin (2013), Machdar et al. (2018), Malik and Shukla (2019) and Bera et al. (2021)). Then with the development of technology as well as the data source, DRASTIC model has been revised/modified (Wang et al. 2022; Khosravi et al. 2021; Pal et al. 2022) in many forms: (i) DRASTICA which includes the anthropogenic influence (Singh 2015) and gives the highest importance to that influencing factor (in the present study, agriculture is considered as anthropogenic influence, and soil type is the most affecting factor to agriculture and therefore highest weight is given to soil types and model is named as DRASTIC_AGRI); (ii) DRASTIC_LU in which an additional parameter, land use (LU), is used (present study; Saha and Alam 2014); (iii) pesticide_DRASTIC which is a pollutant’s oriented model (Barbulescu 2020); (iv) DRASTICM which includes the lineament influence denoted by M (Mendoza and Barmen 2006); and (v) and in general CDRASTIC model where C denotes composite model (Malakootian and Nojari 2020), etc. to improve the accuracy of results. Sarkar and Pal (2021) applied agricultural DRASTIC_AGRI and modified DRASTIC (which uses additional parameters such as LU and other composite parameters) to assess the groundwater vulnerability of the Malda area in West Bengal, India. Standard weights (1–5) are used to indicate the use of pesticides in agricultural activities (Aller et al. 1985). Pesticide applications were said to occur in agricultural DRASTIC_AGRI, and it is utilised in agricultural regions, while the original DRASTIC is employed in industrial and urban locations. They justified that DRASIC_AGRI was used because most of the study area is covered by agriculture land. In the result they observed that 43.16% of areas have low vulnerability (ranging from 95 to 143), and 20.51% of areas have high vulnerability (ranging from 170 to 197). Baghapour et al. (2016) applied DRASTIC_LU to assess the groundwater vulnerability (nitrate pollution) in the Shiraz aquifer located in Fars province, Iran. It was observed that the nitrate index ranged from 6.4 to 185 and was classified as very low (70), low (70–110) and medium (110–145). Further, it was interpreted that 6.45% of the total area is under moderate vulnerability, and the remaining 81.9% are under very low to low vulnerability. They concluded that nitrate concentration is high with a high volume of agricultural activities (use of pesticides) and shallow groundwater depth (rise of groundwater level), and to overcome the nitrate pollution of groundwater, they suggested some measures, which include an effective drainage system (improvement of the existing drainage system) in the region as well as improvements in fertiliser management (plantation of crops with high nitrate use efficiency). In the present study region, where the major rivers are prone to siltation and sedimentation due to several factors (primarily soil erosion and loss due to urbanisation, landslides and pesticides from agricultural practises), it is very likely that the

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groundwater is contaminated. Therefore, in this chapter, an assessment of the groundwater vulnerability (in terms of contamination) of Imphal East district, Manipur, is conducted using a modified DRASTIC model (DRASTIC_AGRI and DRASTIC_LU) with GIS techniques.

3.2

Study Area

Imphal East district (Fig. 3.1) is one of the districts of Manipur (northeastern state of India) that has a total population of 4,52,661 people (Census, 2011) and geographical area of around 709 km2. It is located at an altitude of 790 m above sea level Imphal East, Manipur, India is located at latitude 24.780654 N and longitude 93.967437 E. A subtropical to temperate climate may be found there and the temperature is between 0 °C and 40 °C. The southwest tropical monsoon’s effect is a phenomenon that the region encounters. The monsoon season’s heaviest rain falls between May and August of every year. The region resembles a flat, long, narrow valley with solitary hills that rise up towards the south.

Fig. 3.1 Imphal East district map (MARSAC, Government of Manipur)

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Methodology

DRASTIC model has different modified versions, and the models in this study are two of them IDRASTIC_AGRI and DRASTIC_LU. The data used and equations of DRASTIC_LU model and DRASTIC_AGRI model with theoretical background to assess the groundwater vulnerability in this study are presented in this section.

3.3.1

DRASTIC Model

DRASTIC model is used to evaluate vertical vulnerability mapping, and each letter in this model represents the required input parameters to get the final output. Therefore, D represents depth to water, R as net recharge, A as aquifer media, S as soil media, T as topography, I as impact of vadose zone and C as hydraulic conductivity. It is presumed that all these input parameters significantly affect the health of groundwater (pollution or contamination of groundwater) and play an important role in the overall results. In this model, each component (input parameters) is given a weighting multiplier to balance and enhance its importance. Thus, a scale of 1–5 as weight and a scale of 1–10 as ratings normally are assigned to each input parameter and are provided in Table 3.2 in the Result and Discussion section. After weights and rank are assigned to these input parameters, weighted overlay analysis is performed, and the weighted total of the seven elements results in the final vulnerability index as the DRASTIC index (Di) is given as Di = Dr Dw þ Rr Rw þ Ar Aw þ Sr Sw þ T r T w þ I r I w þ C r C w

ð3:1Þ

where Di is the DRASTIC index for a mapping unit w weighting factor for each parameter r rating for each parameter D, R, A, S, T, I and C, the seven parameters, that is, D is the depth of water, R is the net recharge, A is the aquifer media, S is the soil media, T is the topography, I is the vadose zone impact and C is the hydraulic conductivity, respectively.

3.3.2

DRASTIC_AGRI Model

In this modified model, vulnerability index is generated with different weights from the original DRASTIC model. Since weights are assigned according to the importance of the particular model, highest weight is assigned to the parameter that is important to the agriculture.

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Fig. 3.2 Structure of data used in DRASTIC_AGRI and DRASTIC_LU models

3.3.3

DRASTIC_LU Model

In this modified model, vulnerability index is generated by adding land use/land cover (LULC) to the original DRASTIC model having assigned weights and rating for different parts of LULC. Then, weighted overlay analysis is performed with the eight parameters in GIS platform. DRASTIC_LU vulnerability index is given as DLU = Dr Dw þ Rr Rw þ Ar Aw þ Sr Sw þ T r T w þ I r I w þ C r CW þ Lr Lw

ð3:2Þ

where L represents land use/land cover (Fig. 3.2).

3.3.4

Data Used

The data used (input parameters) with its sources and mode of derivation and extraction is provided in Table 3.1. The description of these input parameters provides the insight meaning of the characteristics possessed. The collected data retrieved from different sources were again processed through GIS tool (ArcGIS®). Field work is conducted to calculate the hydraulic conductivity. Mini disk infiltrometer (MDI) is used for the measurement (Fig. 3.3). It has a suction tube on

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Table 3.1 Sources of data used Parameters (data) Depth to water

Mode Interpolation

Net recharge

Integration

Aquifer media Soil

Digitisation Digitisation

Topography (slope) Impact of vadose zone Hydraulic conductivity

Spatial analyst tool Digitisation

Land use/land cover (LULC)

Spatial analyst tool

Digitisation

Sources Collected data (depth to water table) from the booklet issued by CGWB (Central Groundwater Board), Imphal East, 2018 Rainfall data collected from CGWB, 2018, as per IMD (Indian Meteorological Department) from 2013 to 2017 Aquifer data From booklet – East District by CGWB, 2018 Soil data from Groundwater Information Booklet Imphal East District, Manipur, by the Ministry of Water Resource, 2013 Developed from SRTM data in ArcGIS using spatial analyst tool with resolution30 Collected the lithology data from booklet – Imphal East District by CGWB, 2018 Collected the data by using the mini disk infiltrometer (MDI), performing the experiment in different locations of Imphal East and calculated the hydraulic conductivity value using graph in excel sheet Developed from ESRI data (Sentinel-2 10m,) in ArcGIS using spatial analyst tool for 2021

top (7 cm) of suction regulation tube (10.2 cm) followed by mariotte tube (28 cm) and stainless steel porous disk (4.5 cm diameter, 3 mm thick). Water should be poured in regulation tube up to 10 cm and suction tube is inserted according to soil porosity nature. Volume of the mariotte tube in initial time is noted and volume is noted every 30 s. It can be continued until the difference of the initial and final volume is less than 30 ml. Then the cumulative is calculated by difference of initial and final volume divided by area of the infiltration. Thereafter, hydraulic conductivity is calculated by cumulative infiltration divided by area (from the Van Genuchten parameters according to the suction type and soil type). Different locations were selected for the measurement of hydraulic conductivity in the study region, and Fig. 3.3 shows the MDI at measurement site, that is, Napet Pali (local name).

3.4

Result

In this section, the derived input parameters in the form of raster and vector from the collected ground data will be provided along with the final weighted overlay result of groundwater vulnerability map. All the input parameters were derived using IDW (inverse distance weighting) method in ArcGIS®. The assigned weights and ranks of the parameters are also provided in table.

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Fig. 3.3 Measurement of hydraulic conductivity using MDI at site

3.4.1

Input Parameters

3.4.1.1

Depth of Water

For mapping of parameter, D, depths (m) of certain locations of the study area are collected from CGWB (2018). Depth ranges from 0 to 9 m. Then using the coordinates of the locations, interpolation is done by IDW shown in Fig. 3.4 and classified as per Table 3.2.

3.4.1.2

Net Recharge

Next parameter, R, is calculated by the summation of rainfall for the year 2021. After the summation, the data is added in the tool, and then net recharge mapping is done

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Fig. 3.4 Depth of water (m) map

through IDW using the coordinates of the study area, hence providing the map in Fig. 3.5. The sum of annual rainfall is about 1099 mm in the study area.

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Table 3.2 Weight and rank assigned to the input parameters of DRASTIC-LU model and DRASTIC_AGRI models (Aller et al. 1985) Parameters Depth of water (m)

Net recharge (mm) Aquifer media

Soil media Topography (%) slope)

Impact of vadose zone Hydraulic conductivity (m/d) Land use/land cover (LULC)

3.4.1.3

Range 0–1.5 1.5–4.5 4.5–9 10–99 Sand, medium Sand, coarse Sand, gravel Gravel Loamy sand 0–2 2–6 6–12 12–18 >18 Shale and siltstone Sand, silt and clay 0–4.1 Water body Barren land Wet land Agriculture Built-up Crop land

DRASTIC-LU Weight Rating 5 10 9 7 4 9 3 10 9 8 7 2 6 1 10 9 5 3 1 5 6 3 3 1 5 1 1 1 8 7 7

Agriculture DRASTIC Weight Rating 5 10 9 7 4 9 3 10 9 8 7 5 6 3 10 9 5 3 1 4 6 3 2 1 – – – – – – –

Aquifer Media

Aquifer media is the medium that is beneath the water table of the ground, and Imphal East is mostly covered by sand and gravel (CGWB 2018). Like the other parameters, mapping is done through IDW shown in Fig. 3.6 for aquifer media.

3.4.1.4

Soil Media

Soil media data is collected from CGWB (2018) & Ground water Information (2013) and interpolated using IDW. Soil type of the study area is mostly covered by loamy sand. The map of the soil type is shown in Fig. 3.7.

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Fig. 3.5 Rainfall (mm) map

3.4.1.5

Topography

Topography is the percentage slope. The DEM (digital elevation model) is downloaded from earth explorer website. Using the slope tool in ArcGIS, the percentage has been calculated and further classified as per Table 3.2. The map in Fig. 3.8 represents the slope in percentage of the area ranges from 0 to >18.

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Fig. 3.6 Aquifer media map

3.4.1.6

Impact of Vadose Zone

Impact of vadose zone is the zone just beneath the soil surface which is followed by the aquifer media. For Imphal East, sand silt and clay and shale and siltstone constitute the vadose zone which are shown in Fig. 3.9.

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Fig. 3.7 Soil type map

3.4.1.7

Hydraulic Conductivity

Hydraulic conductivity is calculated using the mini MDI at different locations of the study area. In the field work, the volume change is the tube of the mini disk infiltrometer noted in the time interval of 30 s. Then, the collected data is analysed in excel sheet and value of hydraulic conductivity is provided in m/d. Interpolating the values of it, map is generated provided in Fig. 3.10.

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Fig. 3.8 Slope (%) map

3.4.1.8

Land Use/Land Cover

Another parameter for DRASTIC-LU model, LULC, is included with the seven parameters. And the weight assigned to this parameter is 5 which is the highest range of the assigned weight in DRASTIC model. Different parts of LULC include water body, barren land, wet land, agriculture, built-up and crop land as shown in Fig. 3.11. This parameter is prepared from ESRI data in spatial analyst tool for

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Fig. 3.9 Vadose zone map

2021. After generating all the required parameters, the respective weights and ranks are assigned which are shown in Table 3.2.

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Fig. 3.10 Hydraulic conductivity (m/d) map

3.4.2

DRASTIC_AGRI Vulnerability Index

Using the above seven maps (Figs. 3.4, 3.5, 3.6, 3.7, 3.8, 3.9 and 3.10), a weighted overlay analysis is performed and generated a final map (Fig. 3.11). After reclassifying the seven maps according to their relative rates from 1 to 10 which was given in Table 3.2, the maps are layered over to the raster calculated in spatial analyst tool. As per the assigned weights in Table 3.2, the weights are multiplied with their rates, and then, the DRASTIC_AGRI vulnerability index is calculated and

Groundwater Vulnerability Mapping Using Modified DRASTIC Model:. . . 94°0’0”E

94°10’0”E

IMPHAL EAST

24°50’0”N

24°40’0”N

LULU Water Body Barren land Wet land

24°40’0”N

24°50’0”N

25°0’0”N

N

25°10’0”N

93°50’0”E

57

25°0’0”N

25°10’0”N

3

Agriculture 0 1.5 3

6

93°50’0”E

9

Built up

Kilometers 12

94°0’0”E

Crop land

94°10’0”E

Fig. 3.11 LULC map

is given in Fig. 3.12. It shows different levels of vulnerable area in the study region and is classified into three levels as low vulnerability (8%), moderate vulnerability (12%) and high vulnerability (80%) presented in Fig. 3.11. It is observed that the index ranges from 130 to 173 and the northern part of the district is comparatively high from other parts of the district.

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Fig. 3.12 DRASTIC_AGRI vulnerability index map

3.4.3

DRASTIC-LU Vulnerability Index

Using the eight parameters (Figs. 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10 and 3.11) which are included in the LULC map, an overlay map is performed and a final map is generated (Fig. 3.12). And procedure is repeated as same as the above index, but in this index, LULC is included and the weights assigned to the eight parameters which are different from DRASTIC_AGRI model because the importance of the parameter to the corresponding index is different. But the rating is same as shown in Table 3.2 and DRASTIC-LU index ranges from 120 to 182 (Fig. 3.13).

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Fig. 3.13 DRASTIC_LU index map

3.4.4

Validation

Total dissolved solid (TDS) is the presence of different qualities which include salts, minerals, metals and other dissolved substances in a given volume of water. It is considered to be one of the parameters for health of water. To validate the vulnerability of the study area, the presence of chemical in groundwater like nitrate, ph, TDS, etc. can prove the pollution of groundwater if the index and chemical value is linearly correlated. In thus study, the predicted DRASTIC_AGRI and DRASTIC_LU indexes are plotted with TDS data collected from the Central Ground

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Fig. 3.14 Comparison of modelled DRASTIC indices and TDS value: (a) DRASTIC_AGRI and (b) DRASTIC_LU model Table 3.3 Predicted groundwater vulnerability of DRASTIC_AGRI and DRASTIC_LU model

Vulnerability category Low Medium High Very high

DRASTIC_AGRI Area % 7.236 1.895 18.278 4.787 100.491 26.320 255.794 66.997

DRASTIC_LU Area % 12.351 3.240 19.201 5.038 83.949 22.026 265.638 69.696

Fig. 3.15 Comparison of groundwater vulnerability between DRASTIC_AGRI and DRASTIC_LU models

Water Board (CGWB), Government of India, for the year 2018 and are shown in Fig. 3.14a and b, respectively. Both the figures depicted that the predicted indexes (it directly indicates the quality of groundwater) are linearly correlated (R2 = 0.934, R2 = 0.948) with the collected ground data of TDS (mg/l) and are considered reasonable for modelling and simulation studies.

3.5

Discussion

The predicted groundwater vulnerabilities of DRASTIC_AGRI and DRASTIC_LU are compared and presented in Table 3.3. The area-wise predicted vulnerability is also plotted in Fig. 3.15. Out of the four classified categories (low, medium, high and

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very high) of groundwater vulnerability, in both models, the very category is the highest in area (67%). This is a clear indication that the study region is significantly affected by groundwater contamination and is a warning sign to all concerned stakeholders. Even the high vulnerability category has reached up to 22% (DRASTIC_LU) or 26% (DRASTIC_AGRI). From Fig. 3.15, it depicts that the two methods predict equal amount of vulnerability. As the purpose of the two models is different, they give different ranges of vulnerability. As the R2 value is higher in the DRASTIC_AGRI model when compared with the TDS value than in the other model (DRASTIC_LU), it can be interpreted that DRASTIC_AGRI is slightly more efficient in predicting groundwater vulnerability in this study region. Therefore, DRASTIC_LU is over predicting, which can also be understood from the higher predicted index range by this model, that is, the index range predicted by DRASTIC_LU (index range is 120–182) is higher than that predicted by DRASTIC_AGRI (index range is 130–173), which means that the assumption of this model by giving more emphasis on LULC can be unsuitable. This also indicates that there is no significant effect of LULC in this study region. Farmers and local people can use this data to grow and select suitable crops in the region.

3.6

Future Scope

DRASTIC_LU and DRASTIC_AGRI are modified from the original DRASTIC model, which is a more accurate and better version. Likewise, different modified models are available in GIS technology, and these are the future scope of the model. For advanced technology, the AHP (analytic hierarchy process) method can also be used in this model with different weights and ranks for different parameters of the model. The AHP model considers the effect of each DRASTIC factor on the vulnerability cycle and provides a comparison of the values of the variables, the normalisation and the computing consistency ratio (CR). When applying the AHP method, the power of significance (as per Saaty’s scale) between the two variables is filled in a matrix format using ground truth data, field characteristics and subject matter specialists’ views, which may make the approach more efficient than the other models. Such values have been simplified to reflect the influence of the subjectivity involved in the weight assignment process. In any model, seven parameters will remain the same, which act as base parameters, and in a modified (new model) model, the required parameter, which is more weighted to the contamination of groundwater, is added. Similarly, different methods, that is, (i) overlay and indexbased methods (applied in this present study), (ii) process-based simulation models, (iii) statistical methods and (iv) hybrid methods, can be applied and compared for an efficient result that can be completely relied upon for any management planning. Another future scope would be expanding the study area to other districts of the state so that a larger scenario of groundwater can be understood in the state, which will help in efficient planning of groundwater vulnerability monitoring strategies.

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Conclusion

As the DRASTIC_AGRI index ranges from 130 to 173, it indicates high vulnerability to groundwater pollution and contamination in the region, which may affect agricultural practises and yield. Similarly, the calculated DRASTIC_LU index ranges from 120 to 182, which indicates that groundwater contamination is high and may affect the overall ecosystem of water resource management in the region. Therefore, agricultural practices should be monitored, especially in terms of crop and fertiliser management (reducing or avoiding pesticides), and proper zoning should be done for low vulnerability (pesticides allowed with conditions) and high vulnerability (pesticides free). Then, zone-wise and its agricultural practices may be adopted. It is also not recommended to use the groundwater directly before it is treated, and further to confirm its chemical presence, a laboratory test may be conducted.

References Ahirwar R, Malik MS, Shukla JP (2022) Groundwater vulnerability assessment of Hoshangabad and Budni industrial area, Madhya Pradesh, India, using geospatial techniques. Applied Water Sci 12:250. https://doi.org/10.1007/s13201-022-01771-8 Aller L, Bennett T, Lehr JH, Petty RJ (1985) DRASTIC: A standardized system for evaluating groundwater pollution potential using hydrogeologic settings. US EPA, Environmental Research Laboratory, Ada, Oklahoma, EPA/600/2-85/0108 Baghapour MA, Nobandegani AF, Talebbeydokhti N, Bagherzadeh S, Nadiri AA, Gharekhani M, Chitsazan N (2016) Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain. Iran J Environ Health Sci Eng 14:13 Barbulescu A, Nazzal Y, Howari F (2020) Assessing the Groundwater Quality in the Liwa Area, the United Arab Emirates. MDPI 12:10 Bera A, Mukhopadhyay BP, Chowdhury P, Ghosh A, Biswas S (2021) Groundwater vulnerability assessment using GIS-based DRASTIC model in Nangasai River Basin, India with special emphasis on agricultural contamination. Ecotoxicol Environ Saf 214:1–11 Census (2011) District population report, Government of Manipur Central Ground Water Board (CGWB) (2018) Ministry of Jal Shakti. Aquifer Mapping and Management Plan Imphal East District, Manipur Dregulo AM, Bobylev NG (2021) Integrated Assessment of Groundwater Pollution from the Landfill of Sewage Sludge. J Ecol Eng 22:68–75. https://doi.org/10.12911/22998993/128872 Ersoy AF, Gültekin F (2013) DRASTIC-based methodology for assessing groundwater vulnerability in the Gümüşhacıköy and Merzifon basin (Amasya, Turkey). Earth Res J 17:33–40 Farooqi A, Masuda H, Siddiqui R, Naseem M (2009) Sources of arsenic and fluoride in highly contaminated soils causing groundwater contamination in Punjab, Pakistan. Arch Environ Contam Toxicol 56:693–706 Ground Water Information Booklet Imphal East District, Manipur by Ministry of Water Resources (2013) Gupta LK, Pandey M, Raj PA, Shukla AK (2023) Fine sediment intrusion and its consequences for river ecosystems: a review. J Hazardous Toxic Radioactive Waste 27(1):04022036. https://doi. org/10.1061/(ASCE)HZ.2153-5515.0000729

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Indulekha VP, Thomas CG, Anil KS (2019) Utilization of water hyacinth as livestock feed by ensiling with additives. Indian J Weed Sci 51:67–71 Khosravi K, Sartaj M, Karimi M, Levison J, Lofti A (2021) A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models. Environ Sci Pollut Res 28:50525–50541 Machdar I, Zulfikar T, Rinaldi W, Alfiansyah Y (2018) Assessment of groundwater vulnerability using DRASTIC Model and GIS : A case study of two sub-districts in Banda Aceh city. Indonesia; IOP Conf: Material Sci Eng 334:012032 Malakootian M, Nozari M (2020) GIS-based DRASTIC and composite DRASTIC indices for assessing groundwater vulnerability in the Baghin aquifer, Kerman. Iran J European Geosci Union 20:20–2351 Malik MS, Shukla JP (2019) GIS modeling approach for assessment of groundwater vulnerability in parts of Tawa river catchment area, Hoshangabad, Madhya Pradesh. India Groundw Sustai Dev 9:100249 Mendoza JA, Barmen G (2006) Assessment of groundwater vulnerability in the Rıo Artiguas basin, Nicaragua. Environ Geol 50:569–580. https://doi.org/10.1007/s00254-006-0233-1 Novotny V (1999) Diffuse pollution from agriculture - A worldwide outlook in USA. Water Sci Tech 39:1–3 Pal SC, Chakrabortty R, Arabameri A, Santosh M, Saha A, Chowdhuri I, Roy P, Shit M (2022) Chemical weathering and gully erosion causing land degradation in a complex river basin of Eastern India: an integrated field, analytical and artificial intelligence approach. Nat Hazards 110:847–879. https://doi.org/10.1007/s11069-021-04971-8 Raju NJ (2022) Arsenic in the geo-environment: A review of sources, geochemical processes, toxicity and removal technologies. Environ Res 203:111782. https://doi.org/10.1016/j.envres. 2021.111782 Reynolds T, Barrett S, Dray L, Evans A, Kohler M, Morales MV (2007) Modelling Environmental and Economic Impacts of Aviation: Introducing the Aviation Integrated Modelling Project. 7th AIAA Aviation Tech, Integration and Operations Conf, Northern Ireland, 18–20 September 2007 Saha D, Alam F (2014) Groundwater vulnerability assessment using DRASTIC and Pesticide DRASTIC models in intense agriculture area of the Gangetic plains, India. Environ Monit Assess 186:8741–8763 Saikumar G, Pandey M, Dikshit PKS (2022) Natural river hazards: their impacts and mitigation techniques. In: River dynamics and flood hazards: studies on risk and mitigation. Springer, Singapore, pp 3–16 Sarkar M, Pal C (2021) Application of DRASTIC and Modified DRASTIC Models for Science Modeling Groundwater vulnerability of Malda District in West Bengal. J Indian Soc Remote Sens 49:1201–1219 Singh AK (2015) Advances in Indian Coldwater Fisheries and Aquaculture. J FisheriesSciencescom. 9(3):048–054 Singh NM, Devi TT (2022) Assessment and Identification of Drought Prone Zone in a Low Laying Area by AHP and MIF Method: A GIS Based Study. IOP Conf. Series: Earth and Environmental Science, p 1–12 Urseler N, Bachetti R, Morgante V, Agostini E, Morgante C (2022) Groundwater quality and vulnerability in farms from agricultural-dairy basin of the Argentine Pampas. Environ Sci Pollut Res 29:63655–63673 Wang N, Liu Q, Xie B, Huang X, Xiao D (2022) Tannin-coated PVA/PVP/PEI nanofibrous membrane as a highly effective adsorbent and detoxifier for Cr(VI) contamination in water. Sep Purif Technol 303:122164. https://doi.org/10.1016/j.seppur.2022.122164 Xie J, Liu Y, Wu Y, Li L, Fang J, Lu X (2022) Occurrence, distribution and risk of pharmaceutical and personal care products in the Haihe River sediments, China. Chemosphere 302: 134874. https://doi.org/10.1016/j.chemosphere.2022.134874

Chapter 4

Flood Hazard Mapping Using Hydraulic Models and GIS: A Review Liza G. Kiba, Grace Nengzouzam, and Prem Ranjan

Abstract Floods are a natural event and are among the most frequent and destructive disasters, causing major infrastructure losses and disrupting livelihoods around the world. Floods are most often caused by extreme hydro-metrological and natural forces, but over the past decade, climate change and human response have added new dimensions. There is a wide array of flood risk management methods that can reduce this destruction, which requires estimating flood risks and their impacts. Preventive measures such as efficient land use planning, flood mapping, and implementation of other agronomical and engineering structures are essential in mitigating the hostile impacts of flood. Flood hazard estimation and mapping can be carried out using various methods depending on data, resources, and time availability. In contrast, flood assessment with the creation of the Geographic Information Systems (GIS) database for the flood zone and hydraulic modelling software such as HEC-RAS and HEC-HMS has proven to be useful for flood assessment. GIS can accurately predict the extent of flooding and produce flood maps, as well as flood damage estimation maps and flood hazard maps. Flood hazard maps can be analysed to provide advance warnings for general preparation and, if needed, evacuation. It is, therefore, one of the most significant tools for flood risk management. Keywords Floods · GIS · HEC-RAS · HEC-HMS · Flood hazard maps

L. G. Kiba (✉) Department of Agricultural Engineering, SAS, Nagaland University, Medziphema, India G. Nengzouzam Department of Agricultural Engineering, SET, Nagaland University, Dimapur, India P. Ranjan Department of Agricultural Engineering, NERIST, Nirjuli, Arunachal Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Pandey et al. (eds.), River, Sediment and Hydrological Extremes: Causes, Impacts and Management, Disaster Resilience and Green Growth, https://doi.org/10.1007/978-981-99-4811-6_4

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Introduction

Flood, a natural catastrophe, affects several regions of the globe, both developed and developing countries. Any natural phenomenon can be defined as a hazardous event if it occurs with the likelihood of causing loss or damage settlements. Flood hazards are among the most common and destructive disasters, causing extensive damage and disrupting livelihoods worldwide. The impact of floods can vary worldwide due to geographical, agricultural, and economic reasons. Although flood calamities are primarily caused by natural events, their repercussions have been increased as a result of human activities. Urbanisation in developing countries and the rapid growth of the associated population lead to the increase of uncontrolled and unplanned development activities (Shah et al. 2020). The development activities involve floods, and flood in plain areas in the cities can potentially increase loss of life and damaging properties. Thus, to minimise the risk of flooding and associated hazards and losses, it is essential to disseminate accurate and reliable information to the public in the form of flood inundation maps. The primary purpose of flood risk assessment is to gain a good understanding of the likelihood of floods of a given intensity occurring over a long period of time. Through this approach, individuals can implement precautionary measures and actions to minimise the impact of floods. The mixture of human vulnerability and physical exposures results in flood hazards. It is difficult to control floods, but we can take measures to minimise their impact. Identifying the right measures to deal with floods is a difficult task. The stages involved in flood disaster management include prediction, preparedness, prevention, reduction, and damage assessment. The flood peril areas can be identified by flood hazard assessment and mapping. It also improves flood risk management and disaster preparedness. The anticipated degree and depth of flooding in a particular site under different scenarios can be assessed through flood hazard assessments and mapping. Changing land use planning, creating emergency response plans, implementing specific flood protection measures, etc. are measures that can improve flood management preparedness. Flood risk assessments can be broadened to assess specific risks, taking into account the socio-economic characteristics of exposure areas. The generation of flood inundation map is greatly promoted from the development of modelling and remote sensing (RS) and geographic information system (GIS) techniques (Bera et al. 2012). The flood risk areas can be identified by combining hydrologic models with RS and GIS by using hydrological models such as Hydrologic Engineering Centres-River Analysis System (HEC-RAS) and Hydrologic Engineering Centres-Hydrologic Modelling System (HEC-HMS). Apart from the identification of areas under flood hazard, floods can also be predicted. For flood hazard assessment and mapping, the key components required are digital elevation models (DEMs) for generating the topographical features of the region and hydrological models for simulating several flood events and its effects. Generating flood inundation maps is greatly influenced by the resolution of the DEM, as higher-resolution DEMs tend to produce more reliable and precise maps

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compared to lower-resolution DEMs (Ogania et al. 2019). Apart from DEM, various dataset such as land cover data, soil data, and meteorological data are also needed. GIS software such as ArcGIS, QGIS, and IGIS may be required for generation of maps. This software can also act as a visualisation tool. Topographic data may be gathered (satellite data) or existing topographic datasets may be used. The use of GIS software enables the mapping of the depth and extent of flooding by measuring local land elevations in response to extreme water levels. Hydrological and historical data on floods, precipitation patterns, and climate data are required to simulate flood modelling, and these variables are used to estimate flooding depth and extent in various scenarios. This identification of high-risk flood zones allows planners to improve awareness and response. Integrated approaches that incorporate flood hazard assessments and associated maps can be implemented by land use and development planners to improve flood preparedness, enhance land developments, and increase community awareness. This paper has presented the different case studies related to flood disaster management and successful implementation of GIS for mapping hazard maps. This work reflects the effectiveness and applicability of different flood hazard mapping methodologies. Successful implementation of flood hazard mapping will not only provide essential information on flood hazards but also enhance management and land use planning measures by limiting development in flood-prone areas.

4.2

Methodology

Flood hazard management is a critical task that involves identifying potential floodprone areas and taking preventive measures to minimise flood risk. The following three phases can explain the methodology involved in developing a flood hazard map: (a) preparing/acquiring a DEM using ArcGIS, (b) simulating flood flows for various return periods using hydraulic models, and (c) producing flood risk maps by integrating the output from phase (a) and phase (b). The initial phase is creating a flood hazard map (FHM) consisting of collecting and organising appropriate data. This involves acquiring data on the study region, topography, hydrology, land use, rainfall patterns, and climate data, from a range of sources, such as satellite imagery, ground surveys, and existing databases. The collected data is then processed and structured into an appropriate format for use in flood hazard modelling. The second phase involves the utilising of the collected data to establish a flood hazard model. RS and GIS techniques are utilised to process the data and create a hydrological model that can forecast water behaviour in the area. The main objective of the model is to define and predict the higher-risk area of flooding. The third phase incudes the generation of FHM based on the second phase which shows the areas at higher risk of flooding and the potential degree of the flood. The map can also be used to identify areas where flood mitigation measures are needed, such as constructing flood walls or implementing land use changes. The map can

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Fig. 4.1 Flowchart of flood hazard management

also be used for emergency planning and response, as it helps authorities to identify areas that are most at risk and to develop appropriate measures to mitigate the impact of flooding. The steps involved in flood hazard management using remote sensing (RS)-GIS and hydrological modelling are shown in Fig. 4.1.

4.3

HEC-RAS and HEC-HMS Model

The hydraulic model HEC-RAS, developed by the US Army Corps of Engineers (USACE), is commonly utilised to estimate the hydraulic characteristics of streams and rivers; this model allows the user to input data and obtain output on the screen and conduct further investigations. Besides the energy conservation equation, HEC-RAS needs data on river cross-sections and upstream flow rate to determine the depth and mean velocity of the river (Fan et al. 2009). By using GIS, the variation of water levels along the channel, which can be superimposed on a DEM of the region, can be computed by HEC-RAS to determine the extent and depth of flooding. The hydraulic model of flood-prone areas using HEC-RAS in RS and GIS was created to generate the flood hazard maps for northern Thailand’s Ping River Basin (Duan et al. 2012). The flood-inundated areas and flood depths of Chiang Mai province for the year 2005 were prepared by employing the HEC-RAS

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one-dimensional flood model. The accuracy of model was validated by crosschecking the model outputs with the RS image. In another study carried out for Greece, the flood-inundated area maps were generated for different areas, where both the similarities and differences were showed (Panagoulia et al. 2013). The hydrologic processes for the given return period were simulated by using HEC-HMS software, and hydrographs were prepared. Several simulations related to the hydraulics of open channel flow were conducted using ArcGIS-compatible HEC-RAS software. They concluded that prioritising and planning flood protection measures in the early phase are vital in generating flood inundation maps. HEC-RAS model along with GIS was used for the Mert River Basin, Turkey, to prepare the flood hazard maps (Demir and Kisi 2015). They employed ArcGIS software to digitise the topographical data and finally generate DEM. Using HEC-RAS software, simulation of flood values was performed. Their output was integrated to prepare the flood risk maps. HEC-RAS was integrated with GIS to delineate flood depths and degrees for Nam Phong River in northeast Thailand (Nut and Plermkamon 2015). The steady flow simulated flood along 148 km of the river and floodplain mappings for different return periods were derived. The researchers concluded that incorporating hydraulic simulation with GIS could improve the efficiency and accuracy of floodplain mapping and management. Moreover, ArcGIS and HEC-RAS provide powerful tools for planners and decision-makers. Romali et al. (2018) evaluated the competence of the HEC-HMS model in flood risk assessment by comparing the observed historic data with the simulated result for certain flood events of Segamat Town, Malaysia. Using Nash-Sutcliffe model efficiency as a performance indicator, both model calibration and validation were carried out. The calibration and validation periods were evaluated using NashSutcliffe efficiency values of 0.90 and 0.76, respectively. The one-dimensional HEC-RAS model in combination with GIS was also used to create FHM of Ajay River basin, where parts of Jharkhand and West Bengal contribute to the drainage basin (Chakraborty and Biswas 2020). They classified FHM in five distinct categories based on various return periods, that is, very low, low, moderate, high, and very high. The damage to land use and population was quantified in detail with the aid of the map produced based on distinct classifications. Multispectral data from Landsat-8 OLI and Sentinel-2, as well as DEM data from Aster (30 m) and Cartosat (10 m), were used in HEC-RAS and RAS mapper to make flood inundation maps of the sub-watershed Imphal River Basin in Manipur (Bipinchandra et al. 2019). The study’s results gave a good look at how floods affect the area where the study was done. A framework was made to use GIS, HEC-HMS, and HEC-RAS to model floods on a regional scale in the Indian city of Hyderabad (Rangari et al. 2019). Flood inundation maps were made based on three floods that happened in the city: one in July 1989, one in August 2000, and one in August 2008. Flood inundation maps were made that showed both the areas at risk and the places where flooding was likely to happen.

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HEC-RAS 5.0.7 and Global Flood Monitoring System (GFMS) tools were employed to identify flood risk zones and delineate flood extent in Prayagraj, India, at the conjunction of River Gange and Yamuna (Sangam) (Kumar et al. 2020). When compared, the estimated data was found to be in close proximity to the observed data indicating the applicability of HEC-RAS and GFMS data/tools together.

4.4

Other Methods

Flood risk maps for the Guwahati Municipal Corporation (GMC) Area, Assam, were prepared by performing field surveys and contacting several governmental bodies to collect information and identify the major causes of the flood (Barman and Goswami 2009). Using Erdas and ArcGIS, they generated the FHM by integrating the collected information and presented the flood-vulnerable areas of the study area. Another study was carried out for Dikrong River Basin in Arunachal Pradesh where flood-prone areas were mapped using GIS (Bhadra et al. 2011). A comparison was made between the generated inundation maps with already published maps under Brahmaputra Board Master Plan for the study area. They observed a very low differences ( 0.5, then P is replaced by 1 – P. ERA5 Land monthly gridded rainfall and temperature from 1991 to 2022 is used in this study to derive SPEI. Both indices use the probability density functions to fit the time series (RF for SPI and RF - PET for SPEI) and then use the inverse standard normal distribution to transfer the cumulative probability density functions to the drought index value. Positive/negative values of the SPI and SPEI indicate wet/dry conditions. Different levels of drought based on SPI and SPEI are shown in Table 12.2. R Studio is used to calculate SPI, PET and SPEI provided the inputs ERA 5 rainfall and ERA 5 temperature.

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Run Theory

Drought events and their characteristics, including annual drought frequency (Df), annual drought duration (Dd), drought intensity (Ds) and drought peak (Dp) at each grid, are determined by run theory (Guerrero-Salazar and Yevjevich 1975). The schematic diagram of the determination of drought events and their characteristics based on the SPI, SPEI and SSMI time series is shown in Fig. 12.2. Run theory is applied using multiple thresholds at x0 = - 0.5, x1 = - 1, x2 = 0.5 using three indices where x0, x1 and x2 are indices’ values (Ma et al. 2023). Using these three thresholds at x0, x1, x2,drought events are identified as follows using run theory: Step 1: Check if the monthly SPI/SPEI/SSMI value is below x0. If yes, it indicates occurrence of drought and mark it as a potential drought event. Based on this, drought events E1, E2, E3, E4, E5 and E6 are selected as shown in Fig. 12.2. Step 2: Remove any minor drought event from above selected events in step 1, which only lasts for one month and does not reach the threshold x1. Accordingly E1 is considered as minor drought event and E2, E3, E4, E5 and E6 are screened for next step. Step 3: Combine any adjacent drought events which have an interval of one month and do not reach the threshold x2, into one drought event. Accordingly, E2 and E3 are combined into one event. So, finally from Fig. 12.2, E2–E3, E4, E5 and E6 are the four drought events identified based on run theory with multiple thresholds.

Fig. 12.2 The schematic diagram showing the identification of drought events

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Fig. 12.3 Overall methodology workflow

Once the drought events are identified, drought parameters Df, Dd, Ds and Dp are determined. Annual drought frequency (Df) is the number of drought events in the study period divided by the number of years. Drought duration for a single drought event is the difference between the start time (ts) and the end time (te) of drought event. Annual drought duration (Dd) is the total duration of drought events in months divided by the total number of years. Drought intensity (Ds) for each drought event is determined as sum of SPEI values divided by drought duration for that particular drought event. Drought peak (Dp) at each grid is the smallest SPEI value for the entire time period. The overall methodology of the study is shown in Fig. 12.3 where SPI, SPEI and SSMI are derived using rainfall, temperature and SM data. Run theory is then applied to these three indices and the drought characteristics, Df, Dd, Ds and Dp, are determined.

12.6

Results

In this study, the monthly state averaged SPI, SPEI and SSMI for Telangana state from 2003 to 2022 is calculated and presented in Fig. 12.4. All the three indices have same classification of different classes of drought from mild drought (-1 ≤ SPI/ SPEI/SSMI ≤ -0.5), to moderate drought (-1.5 ≤ SPI/SPEI/SSMI ≤ -1), to severe drought (-2 ≤ SPI/SPEI/SSMI ≤ -1.5) and to extreme drought (SPI/SPEI/SSMI ≤-2). Irrespective of classes of drought (near normal, mild, moderate, severe and extreme), all the indices are able to show a synchronous pattern in representing drought (negative SPI/SPEI/SSMI) or wet conditions (positive SPI/SPEI/SSMI) with a monthly delay of one or two months by SSMI. This time delay by SSMI shows the translation of meteorological drought represented by SPI/SPEI to agricultural drought represented by SSMI. However, many severe meteorological drought events did not lead to agricultural drought. This may be due to water storage or

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Fig. 12.4 Monthly state-averaged time series plot of SPI, SPEI and SSMI in Telangana during the period of 2003–2022

groundwater recharge, and the propagation time of meteorological drought to agricultural drought is different under different land-use types (Zhou et al., 2021). Drought event identification and characterisation are a prerequisite to drought frequency analysis and are related to drought risk often characterised by its duration, frequency, intensity and spatial extent (Xu et al. 2015). In this study, using run theory drought events are identified for Telangana, and annual drought duration (Dd), annual drought frequency (Df), drought intensity (Ds) and drought peak (Dp) are calculated. Figure 12.5 displays the spatial patterns of drought characteristics Dd, Df, Ds and Dp determined from drought events identified by one-month SPI, SPEI and SSMI over Telangana from 2003 to 2022. In Fig.12.5, high meteorological/ agricultural drought risk is represented by blue colour and low meteorological/ agricultural drought risk by yellow colour for all the four drought characteristics. In terms of drought peak (Dp), all the three indices show very low values (blue

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Fig. 12.5 Annual drought duration (Dd), annual drought frequency (Df), drought intensity (Ds) and drought peak (Dp) as determined by multi-threshold run theory

colour). It means in terms of dry only conditions, that is, if there is a drought, there is higher risk of extreme droughts for the entire Telangana represented by SPI/SPEI with SSMI indicating even more intense drought risk at very few regions indicated by yellow colour. In terms of drought frequency (Df) too, all the three indices indicated higher meteorological/agricultural drought risk (blue colour). The spatial distribution of Ds is consistent with that of Dd for all the three indices. For all the four drought characteristics, Dd, Df, Ds and Dp, SPEI showed greater drought risk whereas SPI/SSMI indicated lower drought risk in terms of Dd and Ds. This also suggests that drought occurred more frequently (higher Df) but with shorter duration (lesser Dd by SPI, SSMI) and more severity (higher Dp) in Telangana from 2003 to 2022. For meteorological drought risk analyses, SPEI and SPI showed different spatial pattern. This is due to input parameters provided to calculated SPI and SPEI. SPEI considers both rainfall and precipitation; SPI considers only rainfall. Because of this meteorological drought risk assessed by SPEI based run theory, it should be considered more accurate than that of SPI-based run theory.

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Conclusions

The drought indices, that is, SPI, SPEI and SSMI, are analysed for the relation between meteorological drought and agricultural drought. The translation of meteorological drought to agricultural drought is represented by a time delay of a month or two by SSMI compared to SPI and SPEI. The investigation into the relation between meteorological drought and agricultural drought provides better understanding of the process of drought propagation and could aid in enhancing drought preparedness and alleviation measures. The SPI, SPEI and SSMI are also analysed to identify drought event characterisation over Telangana to assess drought risk in terms of Dd, Df, Ds and Dp. The SPI and SSMI displayed a similar pattern in all the four drought characteristics. All the three indices displayed similar spatial distribution of Ds and Dd. The SPEI characterises higher values in Dd, Df, Ds and Dp which can be largely attributed to increasing PET. In contrast, the SPI did not demonstrate these higher patterns, as it solely considers rainfall and does not show a higher Dd and Ds. This emphasises the significance of PET in explaining the spatial aspects of drought dynamics. These findings indicate that the escalating PET has the potential to intensify drought conditions, indicating a worrisome future considering the projected increase in PET due to a warming climate (Li et al. 2020). Thus, it is plausible that the SPEI may be more appropriate than the SPI index to assess drought risk related to climate change.

12.8

Future Scope and Recommendations

In this study, the time delay between meteorological drought and agricultural drought was seen only by a time series analysis, and any linear or nonlinear relationship between them was not performed. A correlation analysis between both the droughts would have given more in-depth results of how drought is translated from meteorological drought to agricultural drought. Given the influence of climate change and human interventions, the relationship between these two types of droughts and their propagation is complex and is not limited to linear correlation but may also involve nonlinear connections (Leng et al., 2015). Zhou et al. (2021) revealed that the translation time from meteorological to agricultural drought varied across different land-use categories. Thus, it is crucial to account for both linear and nonlinear relationships between different droughts. Drought risk in this study is assessed individually in terms of Dd, Df, Ds and Dp. But the better risk assessment will be calculated by a joint probability distribution between any of these two characteristics (Dd_Df, Dd_Ds, Dd_Dp, Ds_Df, Df_Dp, Dd_Dp) or three characteristics (Dd_Df_Ds, Dd_Df_Dp, Dd_Ds_Dp) from the four drought characteristics. Long-term plans are necessary for reducing the risk of drought, and early warning should be viewed as a means of effectively reducing the increasing susceptibility of

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communities and assets. Regarding early warning systems for drought, it is widely acknowledged that establishing an efficient system is crucial for identifying risks and closely monitoring farmers’ vulnerability levels. A dependable and lasting drought early warning system relies on multi-level governance, institutional setups and frameworks that utilise risk assessment characteristics like Dd, Df, Ds and Dp for gradual hazards like drought.

References Agriculture Action Plan 2021–2022 (2022) Department of Agriculture, Government of Telangana. https://agri.telangana.gov.in/open_record_view.php?ID=959 Anuradha UV (2016) Telangana Drought 2016. South Asia Network on Dams, Rivers and People Bisht DS, Sridhar V, Mishra A, Chatterjee C, Raghuwanshi NS (2019) Drought characterization over India under projected climate scenario. Int J Climatol 39(4):1889–1911 Das S, Das J, Umamahesh NV (2021) Nonstationary modeling of meteorological droughts: application to a region in India. J Hydrol Eng 26. https://doi.org/10.1061/(asce)he.1943-5584. 0002039 Das S, Das J, Umamahesh NV (2022a) Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings. Hydrol Sci J 67:1683–1701. https://doi.org/10. 1080/02626667.2022.2079416 Das S, Das J, Umamahesh NV (2022b) Investigating the propagation of droughts under the influence of large-scale climate indices in India. J Hydrol 610. https://doi.org/10.1016/j. jhydrol.2022.127900 Ding Y, Gong X, Xing Z, Cai H, Zhou Z, Zhang D, Sun P, Shi H (2021) Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China. Agric Water Manag 255:106996 Guerrero-Salazar PLA, Yevjevich VM (1975) Analysis of drought characteristics by the theory of runs (Doctoral dissertation, Colorado State University. Libraries) Hagman G, Beer H, Bendz M, Wijkman A (1984) Prevention better than cure. Report on human and environmental disasters in the Third World. 2 Hinge G, Piplodiya J, Sharma A et al (2022) Evaluation of hybrid wavelet models for regional drought forecasting. Remote Sens 14. https://doi.org/10.3390/rs14246381 Jamro S, Dars GH, Ansari K, Krakauer NY (2019) Spatio-temporal variability of drought in Pakistan using standardized precipitation evapotranspiration index. Appl Sci 9(21):4588 Kim H, Park J, Yoo J, Kim TW (2015) Assessment of drought hazard, vulnerability, and risk: a case study for administrative districts in South Korea. J Hydro Environ Res 9(1):28–35 Kumari C, Shukla R (2023) Nature Based Solutions (NBS) to achieve food security and SDGs in drought prone subtropical area Łabędzki L, Bąk B (2014) Meteorological and agricultural drought indices used in drought monitoring in Poland: a review. Meteorology Hydrology and Water Management. Res Oper Appl 2(2):3–13 Leng G, Tang Q, Rayburg S (2015) Climate change impacts on meteorological, agricultural and hydrological droughts in China. Glob Planet Chang 126:23–34 Li L, She D, Zheng H, Lin P, Yang ZL (2020) Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China. J Hydrometeorol 21(7):1513–1530 Ma Q, Li Y, Liu F, Feng H, Biswas A, Zhang Q (2023) SPEI and multi-threshold run theory based drought analysis using multi-source products in China. J Hydrol 616:128737 McKee TB (1995) Drought monitoring with multiple time scales. In Proceedings of 9th Conference on Applied Climatology, Boston, 1995

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McKee TB, Doesken NJ, Kleist J (1993, January) The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology 17(22): 179–183) Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216 Muñoz-Sabater J, Dutra E, Agustí-Panareda A, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S, Hersbach H, Martens B, Miralles DG, Piles M, Rodríguez-Fernández NJ, Zsoter E, Buontempo C, Thépaut JN (2021) ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst Sci Data 13(9):4349–4383 Park S, Im J, Jang E, Rhee J (2016) Drought assessment and monitoring through blending of multisensor indices using machine learning approaches for different climate regions. Agric For Meteorol 216:157–169 Saikumar G, Pandey M, Dikshit PKS (2023) Natural river hazards: their impacts and mitigation techniques. p 3–16. https://doi.org/10.1007/978-981-19-7100-6_1 Sharma A, Goyal MK (2020) Assessment of drought trend and variability in India using wavelet transform. Hydrol Sci J 65:1539–1554. https://doi.org/10.1080/02626667.2020.1754422 Shi H, Chen J, Li T, Wang G (2020) A new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data. J Hydro Environ Res 28:1–14 Tao R, Zhang K (2020) PDSI-based analysis of characteristics and spatiotemporal changes of meteorological drought in China from 1982 to 2015. Water Resour Protect 36(5):50–56 Telangana State at a Glance 2021 by Directorate of Economics and Statistics, Government of Telangana, https://www.telangana.gov.in/PDFDocuments/Telangana-State-at-aGlance-2021.pdf Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23(7): 1696–1718 Wang X, Zhang Y, Feng X, Feng Y, Xue Y, Pan N (2017) Analysis and application of drought characteristics based on run theory and Copula function. Trans Chin Soc Agric Eng 33(10): 206–214 Wilhite DA (2000) Chapter 1 drought as a natural hazard: concepts and definitions. Drought Mitigation Center Faculty Publications, p 69 Wu M, Li Y, Hu W, Yao N, Li L, Liu DL (2020) Spatiotemporal variability of standardized precipitation evapotranspiration index in mainland China over 1961–2016. Int J Climatol 40(11):4781–4799 Xu K, Yang D, Xu X, Lei H (2015) Copula based drought frequency analysis considering the spatio-temporal variability in Southwest China. J Hydrol 527:630–640 Yao N, Li Y, Lei T, Peng L (2018) Drought evolution, severity and trends in mainland China over 1961–2013. Sci Total Environ 616:73–89 Zhang L, Wang Y, Chen Y, Bai Y, Zhang Q (2020) Drought risk assessment in Central Asia using a probabilistic copula function approach. Water 12(2):421 Zhou Y, Liu L, Zhou P, Jin J, Li J, Wu C (2014) Identification of drought and frequency analysis of drought characteristics based on palmer drought severity index model. Trans Chin Soc Agric Eng 30(23):174–184 Zhou K, Li J, Zhang T, Kang A (2021) The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China. Agric Water Manag 243:106479

Chapter 13

Drought Modeling Through Drought Indices in GIS Environment: A Case Study of Thoubal District, Manipur, India Denish Okram and Thiyam Tamphasana Devi

Abstract In this study, drought-affected zones were modeled using satellite data and geographical information system (GIS) techniques in Thoubal district, Manipur (north eastern part of India), from 2013 to 2021. Different drought indices, that is, standard precipitation index (SPI), temperature condition index (TCI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), NDVI deviation (DevNDVI), and vegetation health index (VHI), were used in the modeling. From the results, the study area has been classified into five classes (severely dry, moderately dry, near normal, mildly wet, and moderately wet), and mostly the study area witnesses two drought conditions, that is, moderate drought and near normal. Thus, drought-like conditions occurred in the years 2015, 2016, 2018, 2019, 2020, and 2021 while in the years 2013 and 2014, the study area experienced both moderate drought and near normal condition in different parts of the district, and in 2017, the whole district received a sufficient amount of precipitation and experienced a near normal condition. A comparison of the predicted results with the collected data was done, and it was observed that the crop yield is high when the near normal condition is predicted for the year 2017. In support of the validation of predicted results, a community opinion-based survey was also conducted by interacting with the local people at various parts of the study area. Their opinions of crop production affected either by drought or flood are found to be relevant with the predicted results of the present study. Keywords Drought modeling · Drought indices GIS techniques · Thoubal district · Satellite data

D. Okram · T. T. Devi (✉) Department of civil Engineering, National Institute of Technology, Manipur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Pandey et al. (eds.), River, Sediment and Hydrological Extremes: Causes, Impacts and Management, Disaster Resilience and Green Growth, https://doi.org/10.1007/978-981-99-4811-6_13

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13.1

D. Okram and T. T. Devi

Introduction

River, sediment, and hydrological extremes are closely linked, as these strong interrelationships can lead to a range of natural hazards (Das et al. 2022a, b, c) such as reduced water availability, impacts on agricultural productivity, damage to infrastructure, loss of life, and injury. The impacts of these hazards can be significant and long-lasting and can have far-reaching economic, social, and environmental consequences. Rivers and its tributaries are similar with the veins of human and carry everything along with the flow including sediments, which is one of the most problems creating discharges. Sediment discharge in rivers is due to soil erosion in upstream side of the river, which is caused by the escalating anthropogenic influence (Saikumar et al. 2022). Long-term continuous deposition of sediments decreases water depth and becomes a caused for hydrological extreme events such as drought. Drought is one of the hydrological extreme events that impact the ecosystem of water resources management and living things on earth in the short term as well as in the long term (Saikumar et al. 2022). It is defined as prolonged shortages of water supply due to the lack of significant precipitation, over utilization of water, siltation, and sedimentation of rivers and lakes and interrupted weather patterns that disturb the water cycle. It is also a slow recurring and unpredictable disaster that leads to serious impacts on livestocks, humans, and the environment throughout the world and is more powerful than other natural disasters (Temesgen et al. 2001). Meteorological (Das et al. 2021a) and agricultural drought (Goyal and Sharma 2016; Sharma and Goyal 2020; Das and Umamahesh 2022) causes change in climate patterns, precipitation deficits, and increased evapotranspiration. Managing these hazards requires a comprehensive approach that considers the underlying causes of hydrological extremes (Das et al. 2022a, b; c). This includes implementing measures such as water conservation practices, sediment management strategies, and drought control structures to mitigate the impacts of these phenomena. It also involves improved communication and collaboration between stakeholders, including governments, water resource managers, and communities. Some of the most challenging problems that human societies currently encounter are rising food demand due to increase in population (Das et al. 2022a, b, c) and environmental stressors, which have prompted new studies to look at how droughts affect food production (Orimoloye 2022). Another major problem faced all over the world is the drying up of rivers and lakes (Bond et al. 2008; Wu et al. 2021; Gupta et al. 2023), which are the main source of water supply to all the living lives. India is also a country that depends on agriculture, and India’s agriculture is solely dependent on fresh water resources, which is likely a result of irregular rainfall patterns and extended droughts (Purohit et al. 2021; Das et al. 2022a, b, c; Rawat et al. 2022). Today, several technologies are developed to predict and model the drought pattern, and one of the most reliable and effective is integrated approach of satellite data with geographical information system (GIS) techniques (Yin et al. 2014; Singh and Devi 2022; Zhao et al. 2022) through drought indices (Ihinegbu and Ogunwumi 2022; Zhang et al. 2023) such as standard precipitation index (SPI), normalized

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difference vegetation index (NDVI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), etc. or through multi criteria decision-making methods such as analytic hierarchy process (AHP) and multiinfluencing factors (MIF). Drought modeling can help inform the development of effective management strategies by providing information on the onset, duration, and severity of drought conditions (Das et al. 2022a, b, c; Sharma and Goyal 2020). This information can be used to inform decision-makers about water allocation, drought preparedness measures, and the implementation of drought response plans. Unlike many natural disasters, drought is barely noticeable and is difficult to recognize when the drought will start, and the end of a drought can take around days, months, or even more as the onset of the drought is gradual (Mosley 2015; Das et al. 2021a; b). Most of the droughts occur when usual weather patterns are disturbed, which leads to a drastic change in the water cycle. Therefore, nowadays, satellite-based remote sensing techniques are used to assess high spatial resolution and high temporal resolution for observing the Earth (Gao et al. 2021). The surface characteristics of land and atmosphere can be derived from remotely sensed images. Currently, researchers are demanding for an increasing development of remote sensing data (high-resolution images) for effective drought monitoring. Hammouri and El-Naqa (2007) conducted a study on the assessment of drought conditions prevailing in Amman-Zarqa basin, northern Jordan, using different drought indices using GIS and remote sensing techniques. In this study, the drought was assessed using two different indices (SPI and NDVI). Using SPI, drought severity has been analyzed, and for selected rainfall stations, the annual SPI values for 6 and 12 months from 1975 to 2000 have been analyzed and found an important phenomenon that the dry seasons return in a similar way year after year. The months of October, November, December, January, and February were used in an investigation of NDVI drought severity for the years 1981–2003. According to the study’s findings, the Amman-Zarqa basin is now experiencing drought conditions. Kloos et al. (2021) in the south east region of Germany, Central Europe, conducted research on agricultural drought detection using moderate-resolution imaging spectro-radiometer (MODIS)-based vegetation health indices. The main goal of this project is to monitor agricultural drought utilizing (MODIS) NDVI, land surface temperature (LST), TCI, VCI, and VHIon water scarce regions and the scope where these drought indices can be used to identify drought conditions. In order to assess the derived drought indices VCI, TCI, and VHI, soil moisture index data from 2001 to 2020, combined with yield data for agricultural crops and land use data, were employed, and it is observed that in the years 2003, 2015, and 2018, the study area was unusually hot or dry, enduring severe drought conditions, which lead to a great loss in agriculture. Abuzar et al. (2017) conducted a study on Drought Risk Assessment Using Remote Sensing and GIS: A Case Study of District Khushab, Pakistan. By the use of temporal images from NDVI (2003, 2009, and 2015) based on Landsat enhanced thematic mapper (ETM) and meteorological based SPI, this work makes an effort to identify areas that are geographically and temporally at risk for drought in agriculture, in addition to drought due to weather. The study area was separated into three zones: no drought, slight drought, and moderate drought. Between the

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NDVI, SPI, and rainfall anomaly, a correlation analysis was conducted. A spatial temporal drought risk map was created after correlating rainfall and NDVI. The vegetation cover classes were calculated using this NDVI, and a trend in their shift was also discovered. Using SPI values over a 15-year period, drought risk was detected. NDVI and SPI readings were used in a linear combination weighted approach to evaluate the impact of the drought. The results showed that 41% of the district has no drought, 28% has a slight drought, and 30% has a moderate drought. According to the study, the southern portion of District Khushab had a deficit in rainfall and little vegetation, making it the region with the highest prevalence of drought. Patil et al. (2021) conducted a study on the analysis of agricultural drought intensity and geographic extent in Manganga watershed of Maharashtra, India. The major goal of the study is to use VHI, which incorporates NDVI, VCI, LST, and TCI, to examine the severity of the agricultural drought. Clear Landsat satellite data availability for the research area is taken into account for the temporal analysis of drought. In this work, Landsat data are used to examine the Vegetation Health Index during the dry seasons of 2001, 2010, 2015, 2017, and 2019. The analysis makes precise measurements of how changes in the drought analysis affect the NDVI, LST, and VHI using satellite data, and it is found that mild and moderate drought conditions are predominant across the entire study area in exception of the nearby areas along the river. Singh and Devi (2022) conducted a study on estimating drought-prone areas in low-lying topography of India’s north-eastern region (Imphal west district in Manipur state). In their study, it was observed in the year 2019 that a condition similar to drought with a terrible amount of available surface water arises, which seriously affects the agriculture and to the livelihood of the region. To forecast drought-prone areas, two methods were used, that is, AHP and MIF. The drought zone region was summarized considering several parameters (like rainfall, temperature, slope, infiltration, vegetation cover, density, soil) as acute (22.8% by AHP and 39.4% by MIF), moderate (60.1% and 54.7%), critical (16.1% and 5.5%), and extreme (0.9% and 0.3%). After analyzing all the data, it is recommended that MIF approach is more precise displaying 43.7% of drought zones are acute and 51.32% are moderate drought-prone areas. The aim of this study is to evaluate drought using satellite data and GIS techniques in Thoubal district, Manipur, and the main objective of this study is to identify and map drought-prone area using drought indices, that is, SPI, TCI, NDVI, VCI, VHI, and DevNDVI(NDVI deviation) for the nine conjugative years, that is, from 2013 to 2021.

13.2

Study Area

Thoubal district (Fig. 13.1) is one of Manipur’s districts in northeastern India. The district is located in eastern Manipur Valley, where it makes up a bigger portion of the state. The district covers an area of 324 km2. It is located between 23°45′–24°45’ N latitudes and 93°45′–94°15′ E longitudes. The district is generally located at an

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Fig. 13.1 Location of study area (Thoubal District)

Fig. 13.2 DEM (left) and slope map (right)

altitude of 790 meters above mean sea level. The district hardly has some hillocks and hills with allow height. Of these, Punam Hill is located at a height of 1009 m above sea level. Fig. 13.2 shows the digital elevation model (DEM) and slope of the study area.

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The major rivers in the study area are the Imphal river originating from Senapati district and the Thoubal river emerging from the hill ranges of Ukhrul district flows through Thoubal district. It has a moderate climate varying seasonally. The major source of income for the population of Manipur depends on agriculture and the activities concerning to it. Seventy percent of the population in the district is associated with farming since the topography of Thoubal district facilitates irrigation significantly. Sugarcane, pineapple, and rice are the most cultivated crops. Animal husbandry and fishing also support to the economy of the district.

13.3

Methodology

The study is conducted using two different sources, that is, metrological data and satellite data, and Fig. 13.3 displays a conceptual breakdown of the methodology that was used. Metrological data has been collected for nine conjugative years from 2013 to 2021. Annual rainfall for nine rain stations has been used to derive SPI. In our study area, there is only one rainfall station, so we have collected other eight rainfall stations from another district as well. For every station, SPI has been

Fig. 13.3 Flowchart of methodology

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calculated, and those calculated SPI are being interpolated with the help of inverse distance weighting (IDW) through GIS tool (ArcGIS®). From the interpolated SPI map, our study area is been extracted. From the satellite data again, two sources have been used: (i) MODIS data and (ii) Landsat-8 data. MODIS data has been collected for 9 years (2013–2021), and from those data, LST (maximum and minimum) has been derived, and using a formula given in Eq. 13.3, TCI from 2013 to 2021 has been calculated. From the Landsat-8 data, NDVI has been derived using GIS tool for nine conjugative years, that is, 2013–2021. From NDVI, VCI and DevNDVI have been calculated for the same 9 years, and from TCI and VCI, VHI is again calculated. All the indices have been reclassified, and using weighted overlay tool, drought-prone zone is predicted.

13.3.1

Drought Indices

13.3.1.1

SPI

The SPI is a drought monitoring precipitation data over a period of time. It has an intensity scale where the SPI positive values denote wet conditions and the SPI negative values show drought conditions. Its purpose is to standardize the rarity of current drought. The formula of SPI is given in Eq. 13.1 as: SPI =

ðX i - X i σ

mean Þ

ð13:1Þ

where Xi = significant precipitation, Ximean = average precipitation, σ = standard deviation of the selected time. The range of SPI value is given in Table 13.1.

13.3.1.2

NDVI

The NDVI is a commonly used vegetation index to understand vegetation health. It computes the difference between visible and near-infrared to determine the density of green vegetation. High NDVI values show dense green vegetation, while the lesser values denote sparse vegetation like barren areas, snow, or sand. The formula used to compute NDVI is as shown in Eq. 13.2: NDVI =

ðNIR - Red Þ ðNIR þ RedÞ

ð13:2Þ

where NIR = near-infrared light and Red = visible red light. The range of NDVI value is given in Table 13.1.

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Table 13.1 Meteorological drought classification using SPI values (McKee et al. 1993), NDVI (Aziz et al. 2018) and TCI, VCI, VHI (Bhuiyan et al. 2008), DevNDVI (Berhan et al. et al. 2011) TCI, VCI, VHI. ≥40

0.2–0.4

Moderate